Random stability control method and system for electronic mechanical braking system and automobile

Abstract

The invention relates to a random stability control method and system of an electronic mechanical braking system and an automobile in the technical field of automobile braking. The control method comprises the steps that under the calibrated brake working condition, brake pressure control is conducted on a brake motor, specifically, the target brake pressure # imgabs0 # output by the brake motor is calculated according to the wheel angular velocity omega at the initial moment of automobile brake and the brake pressure FN on a brake block, and the brake pressure output to the brake block by the brake motor is adjusted to # imgabs1 #. The method is used for conducting brake pressure control on the brake motor according to omega and FN under the calibrated brake working condition, automobile brake safety is guaranteed, meanwhile, automobile brake chatter is effectively restrained, the automobile brake performance is improved, and therefore the problem that in the prior art, while automobile brake safety is guaranteed, the automobile brake chatter is effectively restrained is solved. And the technical problem of automobile braking chatter suppression is effectively solved.

Description

Technical Field

[0001] The present invention relates to a random stability control method in the field of automobile braking technology, and in particular to a random stability control method for an electromechanical braking system, an electromechanical braking method using the random stability control method, a random stability control system for implementing the random stability control method, and an automobile equipped with the random stability control system. Background Art

[0002] Brake judder is a detrimental vibration phenomenon primarily caused by system friction, typically occurring during low-speed creep conditions. With the surge in vehicle ownership and increasing urban road congestion, the number of braking conditions has increased significantly. Brake judder, resulting in increasingly severe issues such as vehicle vibration and wear on the accelerator brake, has seriously impacted vehicle dynamics. Therefore, to meet consumers' demand for high-quality automotive products, research on the instability mechanisms and control methods of brake judder has garnered widespread attention from both OEMs and consumers.

[0003] In recent years, the development of automotive electromechanical braking systems has provided technical support for the precise regulation of brake pressure, thereby achieving better suppression of vehicle brake judder. Existing methods for suppressing vehicle brake judder primarily involve structural optimization and feedback control, with feedback control primarily achieved through regulating the vehicle's brake pressure. However, accurate identification of the vehicle's braking intention is a crucial prerequisite for ensuring vehicle braking stability and safety. Existing technologies for adjusting brake pressure conflict with the driver's braking intention, increasing safety risks during the braking process. Furthermore, due to issues such as parameter uncertainty in the vehicle's braking system and hysteresis in hydraulic brake pressure, the stability of the vehicle's brake judder system is difficult to precisely regulate. Summary of the Invention

[0004] In order to solve the technical problem that traditional technologies are difficult to effectively suppress automobile braking vibration while ensuring automobile braking safety, the present invention discloses a random stability control method for an electronic mechanical braking system, an electronic mechanical braking method using the random stability control method, a random stability control system for implementing the random stability control method, and an automobile equipped with the random stability control system.

[0005] The present invention is implemented by the following technical solution: a method for controlling random stability of an electromechanical braking system, comprising the following steps: Under the calibrated braking conditions, the brake motor is braked to control the brake pressure: according to the wheel angular velocity at the initial braking of the vehicle oh and brake pressure on the brake pads F N Calculate the target brake pressure output by the brake motor and adjust the brake pressure output from the brake motor to the brake pad to ; in, , where m 、 c 2 is the mass and damping of the brake pad; r is the random control parameter of the brake disc torsional stiffness; D is Gaussian white noise; k 1. I 、 c 1 are the torsional stiffness, moment of inertia and torsional damping of the brake disc; R It is the equivalent effective radius of the brake pad acting on the brake disc; k 1 is the linear fitting coefficient of the automobile brake system friction model; a is the attenuation coefficient; Dm = m s - m k , m k 、 m s They are the sliding friction coefficient and static friction coefficient between the brake disc and the brake pad respectively.

[0006] As a further improvement of the above scheme, the random stability control method also includes: Receive the vehicle's braking intention when the vehicle brakes initially j , and judge j Whether it belongs to the calibrated braking condition, if so, the brake pressure of the brake motor is controlled, otherwise, the brake pressure of the brake motor does not need to be controlled.

[0007] Furthermore, the vehicle braking intention is identified by the following identification method: Collect the vehicle's initial braking speed v , brake pedal displacement ratio k ; according to v and k Obtaining vehicle braking intention based on fuzzy algorithm j , to varying degrees j Represents different braking conditions.

[0008] Preferably, the vehicle braking intention j The identification method further includes the following steps: according to v and k Query the fuzzy rule table to get j ; Among them, the fuzzy rule table is v 、 k As input variables,j As the output variable, fuzzy processing v 、 k and j The fuzzy reasoning rules are established.

[0009] More preferably, the design method of the fuzzy rule table includes the following steps: Fuzzy processing: divide the input variables into multiple levels, divide the output variables into multiple levels, and assign value ranges to each interval of the input variables and output variables; Fuzzy reasoning: establish a fuzzy rule table for each interval value range; Defuzzification: Defuzzify the fuzzy reasoning results in the fuzzy rule table to obtain the defuzzified vehicle brake vibration patterns, which represent different braking conditions.

[0010] As a further improvement of the above solution, the target brake pressure The design method includes the following steps: Based on the wheel angular velocity signal at the initial stage of vehicle braking oh and brake pressure signal F N , a two-degree-of-freedom dynamic model considering the brake disc angle and brake pad displacement is established: (3) Where, x、 、 They are the displacement, velocity and acceleration of the brake pad respectively; i 、 、 They are the rotation angle, angular velocity and acceleration of the brake disc; x ( t ) is the strength of 2 D Gaussian white noise; k 2 is the brake pad stiffness; F μ is the braking friction force on the brake disc; The cubic expression of the braking friction force is simplified to: (4) Where, , , , , k 2 is the cubic fitting coefficient of the automobile brake system friction model; make , , transform formula (3) into (5) Where, is the Hamiltonian function of the vehicle braking system, , , , , ; , , , , , , , , , B ( t ) is the standard Weiner process; Transform Equation (5) into the average Ito stochastic differential equation, that is, (6) Where, m ( H ) is the drift coefficient, is the diffusion coefficient, and then the average Ito stochastic differential equation (6) is H = 0 linearization processing (7) Where, , is the diffusion coefficient; Maximum Lyapunov exponent of the average Ito stochastic differential equation for: (8) make <0, the random stability boundary expression of the brake pressure with respect to the wheel angular velocity is solved as follows: (9) The target brake pressure takes the larger value of the above results, that is, (10) Where, .

[0011] The present invention also provides an electromechanical braking method, which uses any of the above-mentioned electromechanical braking system random stability control methods to control the braking pressure of the vehicle's brake motor when the vehicle is braking.

[0012] The present invention also provides a stochastic stability control system, comprising: Pressure sensor, used to collect the brake pressure on the brake pad when the car brakes initially F N ; Angular velocity sensor, used to collect the wheel angular velocity at the initial stage of vehicle braking oh ; Braking force electronic control unit, used to control the brake pressure of the brake motor under the calibrated braking conditions: according to the wheel angular velocity at the initial braking of the vehicle oh and brake pressure on the brake pads F N Calculate the target brake pressure output by the brake motor and adjust the brake pressure output from the brake motor to the brake pad to ; in, , where m 、 c 2 is the mass and damping of the brake pad; r is the random control parameter of the brake disc torsional stiffness; D is Gaussian white noise; k 1. I 、 c 1 are the torsional stiffness, moment of inertia and torsional damping of the brake disc; R It is the equivalent effective radius of the brake pad acting on the brake disc; k 1 is the linear fitting coefficient of the automobile brake system friction model; a is the attenuation coefficient; Dm = m s - m k , m k 、 m s They are the sliding friction coefficient and static friction coefficient between the brake disc and the brake pad respectively; As a further improvement of the above scheme, the stochastic stability control system further includes: Displacement sensor, used to collect the brake pedal displacement ratio at the initial braking of the vehicle k ; Vehicle speed sensor, used to collect vehicle speed at the initial braking v ; Braking intention recognition module, used to provide v and k Obtaining vehicle braking intention based on fuzzy algorithm j , to varying degrees j Represents different braking conditions; Among them, the braking force electronic control unit is also used to judge j Whether it belongs to the calibrated braking condition, if so, the brake pressure of the brake motor is controlled, otherwise, the brake pressure of the brake motor does not need to be controlled.

[0013] The present invention also provides a car equipped with any of the above-mentioned random stability control systems, which is used to achieve random stability control of the car when the car brakes.

[0014] Compared with the prior art, the present invention targets light braking conditions (i.e., under calibrated braking conditions) and calculates the wheel angular velocity at the initial stage of vehicle braking. oh and brake pressure on the brake pads F N By controlling the brake pressure of the brake motor, the system effectively suppresses brake judder while ensuring braking safety, thus improving braking performance. This solves the technical problem that traditional technologies have struggled to effectively suppress brake judder while ensuring braking safety.

[0015] The present invention can also first collect signals such as the vehicle speed, wheel angular velocity, brake pedal displacement ratio, and brake pressure at the initial stage of vehicle braking, and divide the braking intention into three braking modes: light braking, moderate braking, and emergency braking. Then, a driver braking intention recognition method is designed using fuzzy inference rules. For light braking conditions, the Lyapunov exponent of the vehicle braking system is obtained based on the theory of stochastic dynamics, and the stability condition of the vehicle braking pressure is solved. On this basis, the electronic mechanical brake pressure is regulated, which effectively suppresses the vehicle braking vibration while ensuring the vehicle braking safety, thereby further improving the vehicle braking performance.

[0016] Therefore, the core advantages of the present invention are as follows: 1) To ensure vehicle braking safety, the braking intention is divided into three braking modes: light braking, moderate braking, and emergency braking. A driver braking intention recognition method is then designed using fuzzy inference rules to control vehicle brake judder under the calibrated braking condition (light braking condition). 2) Based on the theory of stochastic dynamics, the Lyapunov exponent of the vehicle braking system is obtained to solve the stability conditions of the vehicle braking pressure. Based on this, the electromechanical brake pressure is regulated to effectively suppress the vehicle's brake judder while ensuring vehicle braking safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of the working principle of the electronic mechanical braking system of an automobile provided in Example 1 of the present invention.

[0018] Figure 2 for Figure 1 Flowchart of the method for identifying vehicle braking intention adopted by the braking intention recognition module.

[0019] Figure 3 for Figure 2Membership function diagram of vehicle speed, brake pedal displacement ratio, and vehicle braking intention used in the recognition method.

[0020] Figure 4 for Figure 2 Schematic diagram of the vehicle braking pattern inference results used in the recognition method.

[0021] Figure 5 for Figure 1 Flowchart of the stochastic stability control method employed by the central braking electronic control unit.

[0022] Figure 6 For application Figure 1 Schematic diagram of the rear stability boundary results of the electromechanical braking system.

[0023] Figure 7 For different brake pressures Figure 1 Energy probability density diagram of the vehicle braking system.

[0024] Figure 8 This is a flow chart of the random stability control method provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0026] It should be noted that when a component is referred to as being "mounted on" another component, it may be directly on the other component or there may be a central component. When a component is considered to be "set on" another component, it may be directly set on the other component or there may be a central component. When a component is considered to be "fixed to" another component, it may be directly fixed to the other component or there may be a central component.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0028] Example 1 See also Figure 1, which is a schematic diagram of the operating principle of an electronic mechanical braking system for an automobile provided by an embodiment of the present invention. In addition to the electronic mechanical braking system, the automobile may also include a wheel 1, a flange 2, and a brake disc 3. Brake disc 3 is mounted on wheel 1 via flange 2. When brake disc 3 is driven or braked, wheel 1 is also driven or braked accordingly.

[0029] The electromechanical braking system includes a brake pedal 15, brake pads 5, a floating caliper 6, a piston 7, a brake motor 8, and a stochastic stability control system. The brake pedal 15 provides input for the vehicle's braking signals and inputs brake signals of varying strengths. The brake motor 8 outputs varying brake pressures to the brake disc 3 based on the varying strengths of the brake signals. Specifically, the brake pads 5 indirectly brake the wheels 1 by applying pressure to the brake disc 3. The floating caliper 6 drives the braking operation of the brake pads 5. The brake motor 8 drives the floating caliper 6 by driving the movement of the piston 7, thereby controlling the brake pads 5 through the floating caliper 6 to achieve braking control of the wheels 1. Therefore, adjusting the brake pressure output by the brake motor 8 to the brake pads 5 can change the braking distance and speed of the vehicle during braking. Therefore, the electromechanical braking system controls the brake pressure of the vehicle's brake motor 8, achieving braking control of the vehicle's brake disc 3 by adjusting the brake pressure output by the brake motor 8 to the brake pads 5.

[0030] The function of the random stability control system is to be called when the brake motor 8 is braking and under the calibrated braking conditions, and then the vehicle is braked according to the wheel angular velocity at the beginning of the braking. oh and brake pressure on the brake pads F N , calculate the target brake pressure output by the brake motor and adjust the brake pressure output from the brake motor to the brake pad to , achieving the suppression of vehicle brake judder. Therefore, installing a random stability control system allows the vehicle to achieve random stability control during braking. The random stability control system includes a data acquisition unit, a braking force electronic control unit 9, and a braking intention recognition module 10.

[0031] The data acquisition unit includes a displacement sensor 11, a vehicle speed sensor 12, a pressure sensor 13, and an angular velocity sensor 14. The displacement sensor 11 is used to collect the brake pedal displacement ratio at the initial braking of the vehicle. k ; The speed sensor 12 is used to collect the vehicle's initial braking speed v ; Pressure sensor 13 is used to collect the brake pressure on the brake pad when the vehicle brakes initially F N Angular velocity sensor 14 is used to collect the wheel angular velocity of the vehicle when braking initially oh .

[0032] The braking intention recognition module 10 is used to v and k Obtaining vehicle braking intention based on fuzzy algorithm j , to varying degrees j Represents different braking conditions. Figure 2 As shown, the braking intention recognition module 10 uses the vehicle braking intention j The identification method can be: collecting v and k ,according to v and k Query the fuzzy rule table to get j Among them, the fuzzy rule table is v 、 k As input variables, j As the output variable, fuzzy processing v 、 k and j The fuzzy reasoning rules established by the system are as follows. The design method of the fuzzy rule table can include the following steps: fuzzification processing: dividing the input variables into multiple levels, dividing the output variables into multiple levels, and assigning interval value ranges to the input variables and output variables; fuzzy reasoning: establishing a fuzzy rule table for each interval value range; defuzzification: defuzzifying the fuzzy reasoning results in the fuzzy rule table to obtain the defuzzified vehicle brake judder patterns, which represent different braking conditions.

[0033] In this embodiment, the braking intention recognition module 10 executes a driver braking intention recognition method based on fuzzy reasoning, with vehicle speed and brake pedal displacement ratio as input and vehicle braking intention as output. The specific steps of fuzzy reasoning are as follows.

[0034] 1) Fuzzy processing The input variables are divided into four levels {Low, Medium-Low, Medium-High, High}, and the corresponding fuzzy sets are {S, M, L, H}. The output variables are divided into three levels {Mild, Moderate, Urgent}, and the corresponding fuzzy sets are {NL, NM, NH}. Of course, in other embodiments, the input variables can have more levels, or even two or three; similarly, the output variables can have two or more.

[0035] The value ranges of each interval of the input variables are listed in Table 1.

[0036] Table 1 Fuzzy sets of vehicle speed and brake pedal displacement ratio

[0037] The value ranges of each interval of the output variables are listed in Table 2.

[0038] Table 2 Braking intensity fuzzy sets

[0039] The membership function of the vehicle speed, brake pedal displacement ratio, and vehicle braking intention adopts a trapezoidal shape, and its mathematical expression is shown in formula (1). The specific membership function of the present invention is as follows: Figure 3 shown.

[0040] (1) Where, u ( s ) is the membership function, s is the input variable, g 1. g 2. g 3. g 4 are the left boundary, left vertex, right vertex and right boundary of the trapezoidal membership function respectively.

[0041] 2) Fuzzy reasoning Establish a fuzzy rule base. Based on the two input quantities of vehicle speed and brake pedal displacement ratio, the vehicle braking intention Figure one Output quantity, 16 fuzzy rules are established as shown in Table 3.

[0042] Table 3 Fuzzy rule table

[0043] 3) Defuzzification The centroid method is used to defuzzify the above fuzzy reasoning results. The calculation formula is as follows: (2) Where, or 0 is the defuzzified vehicle brake vibration mode, x n is the output variable, m ( x n ) is the output variable x n The corresponding membership degree. The inference result of the automobile braking mode in the embodiment of the present invention can be seen Figure 4 shown.

[0044] The braking intention recognition method employed by the braking intention recognition module 10 is not limited to that of the present invention. In other embodiments, the braking intention recognition module 10 may be omitted, and the braking force electronic control unit 9 may be directly instructed by a calibrated braking condition (i.e., a light braking condition). Under this light braking condition, the braking force electronic control unit 9 is directly triggered to control the brake pressure of the brake motor 8. In this case, the displacement sensor 11 and vehicle speed sensor 12 of the data acquisition unit may also be omitted.

[0045] Even in other embodiments, an unknown braking condition can be directly input to the braking force electronic control unit 9, and then the braking force electronic control unit 9 determines whether the unknown braking condition is a light braking condition, and controls the braking pressure of the brake motor 8 under the light braking condition. For example, the electromechanical braking system random stability control method adopted by the braking force electronic control unit 9 can be used to determine whether the unknown braking condition is a light braking condition. j Whether it belongs to the calibrated braking condition, if so, the brake pressure of the brake motor 8 is controlled, otherwise, the brake pressure of the brake motor 8 does not need to be controlled.

[0046] like Figure 5 As shown, the brake pressure control here can be specifically as follows: collecting the wheel angular velocity at the initial braking of the vehicle oh and brake pressure on the brake pads F N ,according to oh and F N Calculate the target brake pressure output by the brake motor and adjust the brake pressure output from the brake motor to the brake pad to .

[0047] in, , where m 、 c 2 is the mass and damping of the brake pad; r is the random control parameter of the brake disc torsional stiffness; D is Gaussian white noise; k 1. I 、 c 1 are the torsional stiffness, moment of inertia and torsional damping of the brake disc; R It is the equivalent effective radius of the brake pad acting on the brake disc; k 1 is the linear fitting coefficient of the automobile brake system friction model; a is the attenuation coefficient; Dm is an intermediate variable, and its formula is: Dm = m s - m k , m k 、 m s They are the sliding friction coefficient and static friction coefficient between the brake disc and the brake pad respectively.

[0048] Target brake pressure The formula is designed based on two-degree-of-freedom stochastic dynamics modeling. Next, we will introduce the target brake pressure. design method.

[0049] 1) Two-degree-of-freedom stochastic dynamics modeling Based on the wheel angular velocity signal at the initial stage of vehicle braking oh and brake pressure signal F N , a two-degree-of-freedom dynamic model considering the brake disc angle and brake pad displacement is established: (3) Where, i It is the rotation angle of the brake disc, which can also be referred to as the brake disc angle. is the angular velocity of the brake disc, is the acceleration of the brake disc; x is the brake pad displacement, is the speed of the brake pad, is the acceleration of the brake pad; I is the moment of inertia of the brake disc; c 1 is the brake disc torsional damping; k 1 is the brake disc torsional stiffness; r is the random control parameter of the brake disc torsional stiffness; x ( t ) is the strength of 2 D Gaussian white noise; R It is the equivalent effective radius of the brake pad acting on the brake disc; m is the brake pad mass; c 2 is the brake pad damping; k 2 is the brake pad stiffness; F μ is the braking friction force on the brake disc, and its cubic expression can be simplified as: (4) Where, d 0 and Dm They are all intermediate variables, and their formulas are: , Dm = m s - m k , m kis the coefficient of sliding friction between the brake disc and the brake pad, m s is the coefficient of static friction between the brake disc and the brake pad, a is the attenuation coefficient; d 1. d 2. d 3 are all intermediate variables, and their formulas are: , , , k 1 and k 2 are the linear and cubic fitting coefficients of the automobile brake system friction model.

[0050] make , , transform formula (3) into (5) Where, is the Hamiltonian function of the vehicle braking system, m 11 、 m 12 、 m 21 、 m 22 、 s 11 、 d 1. d 2. k 11 、 k 12 、 k 13 、 k 14 、 k 21 、 k 22 、 k 23 They are all intermediate variables, and their formulas are: , , , , ; , , , , , , , , , B ( t ) is the standard Weiner process.

[0051] 3) Stochastic stability boundary solution analysis Since the automobile brake vibration system belongs to a quasi-integrable Hamiltonian system, Equation (5) can be transformed into an average Ito stochastic differential equation, that is, (6) Where, m ( H ) is the drift coefficient, is the diffusion coefficient. Then the averaged Ito stochastic differential equation (6) is H = 0, we can obtain (7) Where, , is the diffusion coefficient; To ensure the stability of the vehicle braking system, the maximum Lyapunov exponent of the average Ito stochastic differential equation is for: (8) when <0, the trivial solution of the automobile braking system is asymptotically stable with a local probability of 1. >0, the trivial solution of the automobile braking system is asymptotically unstable with local probability 1. =0, the vehicle braking system is in a critical stable state.

[0052] make <0, the stochastic stability boundary expression of the brake pressure with respect to the wheel angular velocity can be solved as follows: (9) While reducing brake pressure at different wheel angular velocities helps suppress vehicle judder, this approach can increase braking distance. Therefore, based on the aforementioned driver intent recognition, the brake stability control method proposed in this invention is only triggered under light braking conditions to ensure vehicle safety.

[0053] In addition, since the brake pressure cannot be reduced indefinitely, the brake pressure must be kept at least 50% of the original brake pressure. Therefore, the target brake pressure is the larger value of the above results, that is, (10) Where, .

[0054] Next, the effect of the present invention is verified and explained through a specific example data.

[0055] The embodiment parameters are I =0.65kg•m 2 , c1=1N•m•s / rad, k 1=300000N•m / rad, R =0.12m, m =1.5kg, c 2=1N•s / m, k 2=300000N / m, m s =0.5, m k =0.3, r = 1720N•m / rad, a = 4.6, D= 0.15s, k 1=-0.8598 s / m, k 2=0.2638s 3 / m 3 , the wheel angular velocity can be obtained oh and brake pressure F N The stability boundary of Figure 6 shown.

[0056] by oh =10rad / s as an example, the target braking pressure is F NC =665.28N. To verify the effectiveness of the present invention, the numerical solution of the probability density function of the average Ito stochastic differential equation (6) is obtained as follows: ,in k is the normalization coefficient, , Therefore, the energy probability density of the vehicle braking system under different control parameters and braking pressure is as follows: Figure 7 As shown. When the brake pressure F N =640N, the energy of the automobile brake vibration system converges to the zero equilibrium point of the system, and the system stable solution is probabilistically stable; when the brake pressure F N =700N, the energy probability density of the automobile braking system has a single peak, the system response converges to the probability limit cycle, and the system stable solution is probabilistically unstable. Obviously, the random stability control method of the electromechanical braking system proposed in the present invention is effective.

[0057] Example 2 This embodiment introduces a stochastic stability control system. This differs from the stochastic stability control system of Example 1 in that, in addition to the data acquisition unit of Example 1, this embodiment also includes a controller. The controller implements the functions of the braking intention recognition module 10 and the braking force electronic control unit 9 of Example 1 and integrates these two modules.

[0058] like Figure 8 As shown, the random stability control method adopted by the controller of this embodiment includes the following steps: Collect the vehicle's initial braking speed v , brake pedal displacement ratio k , wheel angular velocity oh and brake pressure on the brake pads F N ; according to v and k Obtaining vehicle braking intention based on fuzzy algorithm j , to varying degrees j Represents different braking conditions; judge j Whether it belongs to the calibrated braking condition, if so, the brake pressure of the brake motor is controlled, otherwise, the brake pressure of the brake motor does not need to be controlled; according to oh and F N Calculate the target brake pressure output by the brake motor ; Adjust the brake pressure output from the brake motor to the brake pad to .

[0059] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0060] The random stability control system of the present invention can realize the functional upgrade of old cars by adding random stability control function to the braking control function. When applied, a wheel angular velocity sensor can be installed on the brake disc of the old car to collect the wheel angular velocity at the initial braking of the car. oh 0 ; Install and collect the brake pedal displacement ratio when the car brakes at the beginning k The displacement sensor 11 is installed to collect the vehicle's initial speed when braking. v Speed ​​sensor 12; used to collect the brake pressure on the brake pad when the car brakes initiallyF N Pressure sensor 13; used to collect the wheel angular velocity when the vehicle brakes initially oh The angular velocity sensor 14 is finally installed in the car, and the braking force electronic control unit 9 and the braking intention recognition module 10 that are linked to the brake motor 8 of the old car are installed in the car, so that the old car can be functionally innovated. Of course, the braking force electronic control unit 9 and the braking intention recognition module 10 can also be not installed, and the functions of the braking force electronic control unit 9 and the braking intention recognition module 10 can be designed into software, so as to patch the on-board system of the old car, so as to complete the functions of the braking force electronic control unit 9 and the braking intention recognition module 10 through the updated on-board system.

[0061] A new car designed with the functions of the present invention may also be installed with the displacement sensor 11, the vehicle speed sensor 12, the pressure sensor 13, and the angular velocity sensor 14 when it leaves the factory. The onboard system of the new car itself can complete the functions of the braking force electronic control unit 9 and the braking intention recognition module 10, and consumers do not need to purchase a random stability control system separately.

[0062] Therefore, the technology of the present invention can be well applied to both new and old cars. Therefore, the present invention is easy to promote and apply, and has high technology conversion value and commercial prospects.

[0063] The above-described embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person skilled in the art would be able to make numerous modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for controlling random stability of an electromechanical brake system, characterized in that: It includes the following steps: Under the calibrated braking conditions, the brake motor is braked to control the brake pressure: according to the wheel angular velocity at the initial braking of the vehicle ω and brake pressure on the brake pads F N Calculate the target brake pressure output by the brake motor and adjust the brake pressure output from the brake motor to the brake pad to ; in, , where m 、 c 2 is the mass and damping of the brake pad; ρ is the random control parameter of the brake disc torsional stiffness; D is Gaussian white noise; k 1. I 、 c 1 are the torsional stiffness, moment of inertia and torsional damping of the brake disc; R It is the equivalent effective radius of the brake pad acting on the brake disc; к 1 is the linear fitting coefficient of the automobile brake system friction model; a is the attenuation coefficient; Δμ = μ s - μ k , μ k 、 μ s They are the sliding friction coefficient and static friction coefficient between the brake disc and the brake pad respectively.

2. The method for controlling random stability of an electromechanical brake system according to claim 1, wherein: The electromechanical brake system random stability control method further includes: Receive the vehicle's braking intention when the vehicle brakes initially j , and judge j Whether it belongs to the calibrated braking condition, if so, the brake pressure of the brake motor is controlled, otherwise, the brake pressure of the brake motor does not need to be controlled.

3. The method for controlling random stability of an electromechanical brake system according to claim 2, wherein: The vehicle's braking intention is identified through the following recognition methods: Collect the vehicle's initial braking speed v , brake pedal displacement ratio k ; according to v and k Obtaining vehicle braking intention based on fuzzy algorithm j , to varying degrees j Represents different braking conditions.

4. The method for controlling random stability of an electromechanical brake system according to claim 3, wherein: Car braking intention j The identification method further includes the following steps: according to v and k Query the fuzzy rule table to get j ; Among them, the fuzzy rule table is v 、 k As input variables, j As the output variable, fuzzy processing v 、 k and j The fuzzy reasoning rules are established.

5. The method for controlling random stability of an electromechanical brake system according to claim 4, wherein: The design method of the fuzzy rule table includes the following steps: Fuzzy processing: divide the input variables into multiple levels, divide the output variables into multiple levels, and assign value ranges to each interval of the input variables and output variables; Fuzzy reasoning: establish a fuzzy rule table for each interval value range; Defuzzification: Defuzzify the fuzzy reasoning results in the fuzzy rule table to obtain the defuzzified vehicle brake vibration patterns, which represent different braking conditions.

6. The method for controlling random stability of an electromechanical brake system according to claim 1, wherein: Target brake pressure The design method includes the following steps: based on ω and F N , a two-degree-of-freedom dynamic model considering the brake disc angle and brake pad displacement is established: （3） Where, x、 、 They are the displacement, velocity and acceleration of the brake pad respectively; θ 、 、 They are the rotation angle, angular velocity and acceleration of the brake disc; ξ ( t ) is the strength of 2 D Gaussian white noise; k 2 is the brake pad stiffness; F μ is the braking friction force on the brake disc; The cubic expression of the braking friction force is simplified to: （4） Where, , , , , к 2 is the cubic fitting coefficient of the automobile brake system friction model; make , , transform formula (3) into （5） Where, is the Hamiltonian function of the vehicle braking system, , , , , ; , , , , , , , , , B ( t ) is the standard Weiner process; Transform Equation (5) into the average Ito stochastic differential equation, that is, （6） Where, m ( H ) is the drift coefficient, is the diffusion coefficient, and then the average Ito stochastic differential equation (6) is H = 0 linearization processing （7） Where, , is the diffusion coefficient; Maximum Lyapunov exponent of the average Ito stochastic differential equation for: （8） make <0, the random stability boundary expression of the brake pressure with respect to the wheel angular velocity is solved as follows: （9） Target brake pressure: （10） Where, .

7. An electromechanical braking method, characterized in that: When the vehicle brakes, the random stability control method of the electromechanical braking system as described in any one of claims 1 to 6 is adopted to adjust the braking pressure output by the brake motor to the brake pad to achieve braking control of the vehicle's brake disc.

8. A random stability control system, characterized in that: It includes: Pressure sensor, used to collect the brake pressure on the brake pad when the car brakes initially F N ; Angular velocity sensor, used to collect the wheel angular velocity at the initial stage of vehicle braking ω ; Braking force electronic control unit, used to control the brake pressure of the brake motor under the calibrated braking conditions: according to the wheel angular velocity at the initial braking of the vehicle ω and brake pressure on the brake pads F N Calculate the target brake pressure output by the brake motor and adjust the brake pressure output from the brake motor to the brake pad to ; in, , where m 、 c 2 is the mass and damping of the brake pad; ρ is the random control parameter of the brake disc torsional stiffness; D is Gaussian white noise; k 1. I 、 c 1 are the torsional stiffness, moment of inertia and torsional damping of the brake disc; R It is the equivalent effective radius of the brake pad acting on the brake disc; к 1 is the linear fitting coefficient of the automobile brake system friction model; a is the attenuation coefficient; Δμ = μ s - μ k , μ k 、 μ s They are the sliding friction coefficient and static friction coefficient between the brake disc and the brake pad respectively.

9. The stochastic stability control system according to claim 8, characterized in that: The stochastic stability control system also includes: Displacement sensor, used to collect the brake pedal displacement ratio at the initial braking of the vehicle k ; Vehicle speed sensor, used to collect vehicle speed at the initial braking v ; Braking intention recognition module, used to v and k Obtaining vehicle braking intention based on fuzzy algorithm j , to varying degrees j Represents different braking conditions; Among them, the braking force electronic control unit is also used to judge j Whether it belongs to the calibrated braking condition, if so, the brake pressure of the brake motor is controlled, otherwise, the brake pressure of the brake motor does not need to be controlled.

10. An automobile, characterized in that: It is equipped with a random stability control system as claimed in claim 8 or 9, which is used to realize random stability control of the vehicle when the vehicle brakes.

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