Electronic mechanical brake system random stability control method, system and automobile

Abstract

The application relates to a random stability control method of an electronic mechanical brake system, a system thereof and an automobile in the field of automobile brake technology. The control method comprises the following steps: under a calibrated brake working condition, brake pressure control is performed on a brake motor; according to the wheel angular velocity and the brake pressure on the brake block at the initial time of automobile braking ω and brake pressure on the brake block F N The target brake pressure output by the brake motor is calculated, and the brake pressure output by the brake motor on the brake block is adjusted to the target brake pressure. The application is used for, under a calibrated brake working condition, performing brake pressure control on the brake motor according to ω and F N The brake pressure control is performed on the brake motor, automobile brake safety is guaranteed, automobile brake chatter is effectively inhibited, and automobile brake performance is improved, so that the technical problem that a traditional technology cannot effectively inhibit automobile brake chatter while guaranteeing automobile brake safety is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to a random stability control method in the field of automobile braking technology, in particular to an electronic mechanical brake system random stability control method, an electronic mechanical brake method adopting the random stability control method, a random stability control system for implementing the random stability control method, and an automobile installed with the random stability control system. BACKGROUND

[0002] Automobile brake chatter is a harmful vibration phenomenon caused by system friction, usually occurring in low-speed crawling conditions. With the rapid increase of automobile ownership and the intensification of urban road congestion, automobile braking driving conditions have significantly increased, and the problems of vehicle shaking and accelerated brake wear caused by brake chatter have become increasingly prominent, seriously affecting the dynamics of the automobile. Therefore, in order to meet the high-quality pursuit of consumers for automobile products, the instability mechanism and control method of automobile brake chatter have attracted widespread attention from automakers and consumers.

[0003] In recent years, the development of automobile electronic mechanical brake system provides technical support for precise regulation of brake pressure, so that better suppression of automobile brake chatter can be achieved. The existing suppression of automobile brake chatter is mainly divided into two aspects of structure optimization and feedback control, among which feedback control is mainly achieved by adjusting the automobile brake pressure. However, the accurate identification of automobile brake intention is an important prerequisite for ensuring the stability and safety of automobile braking, and the adjustment of brake pressure in the existing technology conflicts with the driver's braking intention, increasing the safety risk in the automobile braking process. At the same time, due to the parameter uncertainty of the automobile brake system and the hysteresis of the hydraulic brake pressure, the stability of the automobile brake chatter system is difficult to accurately control. SUMMARY

[0004] In order to solve the technical problem that the traditional technology cannot effectively realize the suppression of automobile brake chatter while ensuring the safety of automobile braking, the present application discloses an electronic mechanical brake system random stability control method, an electronic mechanical brake method adopting the random stability control method, a random stability control system for implementing the random stability control method, and an automobile installed with the random stability control system.

[0005] The present application adopts the following technical scheme: an electronic mechanical brake system random stability control method, comprising the following steps:

[0006] In the calibrated braking condition, the brake motor is controlled for brake pressure: according to the wheel angular velocity of the automobile at the initial braking ω and the brake pressure on the brake block F N Calculate the target brake pressure output by the brake motor , and the brake pressure output by the brake motor to the brake block is adjusted as ;

[0007] wherein, , wherein, m , c 2 is the mass and damping of the brake block; p is a random control parameter of the brake disc torsional stiffness; D is a Gaussian white noise; k 1, I , c 1 is the torsional stiffness, moment of inertia, and torsional damping of the brake disc, respectively; R is the equivalent action radius of the brake block on the brake disc; k 1 is the first-order fitting coefficient of the friction model of the automobile braking system; a is the attenuation coefficient; Δμ = μ s - μ k , μ k , μ s are the sliding friction coefficient and static friction coefficient between the brake disc and the brake block, respectively.

[0008] As a further improvement of the above scheme, the random stability control method further comprises:

[0009] receiving the automobile braking intention at the initial time of automobile braking j , and determining j whether it belongs to the calibrated braking condition, if yes, controlling the brake pressure of the brake motor, otherwise, no need to control the brake pressure of the brake motor.

[0010] Further, the automobile braking intention is obtained by the following identification method:

[0011] collecting the driving speed at the initial time of automobile braking v , brake pedal displacement ratio k ;

[0012] based on v and k obtaining the automobile braking intention based on fuzzy algorithm j , different degrees of j represent different braking conditions.

[0013] Preferably, the identification method of the automobile braking intention j further comprises the following steps:

[0014] based on v and kThe fuzzy rule table is queried to obtain j ; wherein, the fuzzy rule table is a fuzzy inference rule established by taking v , k as input variables, taking j as output variables, and fuzzily processing v , k and j .

[0015] Further preferably, the design method of the fuzzy rule table comprises the following steps:

[0016] fuzzy processing: dividing the input variables into multiple levels, dividing the output variables into multiple levels, and assigning the value range of each interval of the input variables and the output variables;

[0017] fuzzy inference: establishing the fuzzy rule table for each interval value range;

[0018] defuzzification: defuzzification processing the fuzzy inference result in the fuzzy rule table to obtain the defuzzified automobile brake judder mode, which represents different brake working conditions.

[0019] As a further improvement of the above scheme, the design method of the target brake pressure comprises the following steps:

[0020] based on the wheel angular velocity signal ω and the brake pressure signal F N at the initial time of automobile braking, a two-degree-of-freedom dynamic model considering the brake disc rotation angle and the brake block displacement is established:

[0021] (3)

[0022] wherein, x、 , are the displacement, velocity and acceleration of the brake block, respectively; θ , , are the rotation angle, angular velocity and acceleration of the brake disc, respectively; ξ ( t ) is a Gaussian white noise with a strength of 2 D ; k 2 is the brake block stiffness; F μ is the brake friction force on the brake disc;

[0023] the third order term expression of the brake friction force is simplified as:

[0024] (4)

[0025] wherein, , , , , k 2 is the cubic fitting coefficient of the automobile brake system friction model;

[0026] make , , transform formula (3) into

[0027] (5)

[0028] Where, is the Hamiltonian function of the vehicle braking system, , , , , ; , , , , , , , , , B ( t ) is the standard Weiner process;

[0029] Transform Equation (5) into the average Ito stochastic differential equation, that is,

[0030] (6)

[0031] 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

[0032] (7)

[0033] Where, , is the diffusion coefficient;

[0034] Maximum Lyapunov exponent of the average Ito stochastic differential equation for:

[0035] (8)

[0036] make <0, the random stability boundary expression of the brake pressure with respect to the wheel angular velocity is solved as follows:

[0037] (9)

[0038] The target braking pressure takes the larger value of the above results, i.e.

[0039] (10)

[0040] wherein, .

[0041] The present application also provides an electromechanical braking method, which adopts the above-mentioned random stability control method of the electromechanical braking system to control the braking pressure of the braking motor of the automobile during braking.

[0042] The present application also provides a random stability control system, which comprises:

[0043] a pressure sensor for collecting the braking pressure on the brake block at the initial braking of the automobile F N ;

[0044] an angular velocity sensor for collecting the wheel angular velocity at the initial braking of the automobile ω ;

[0045] a braking force electronic control unit for controlling the braking pressure of the braking motor under the calibrated braking condition: calculating the target braking pressure output by the braking motor according to the wheel angular velocity at the initial braking of the automobile ω and the braking pressure on the brake block F N , and adjusting the braking pressure output by the braking motor to the brake block ; ;

[0046] wherein, , wherein, m , c 2 is the mass and damping of the brake block; p is the random control parameter of the torsional stiffness of the brake disc; D is the Gaussian white noise; k 1, I , c 1 is the torsional stiffness, moment of inertia and torsional damping of the brake disc, respectively; R is the equivalent action radius of the brake block on the brake disc; k 1 is the fitting coefficient of the linear term of the friction model of the automobile braking system; a is the attenuation coefficient; Δμ μ s - μ k , μ k ,​μ s respectively are the sliding friction coefficient and the static friction coefficient between the brake disc and the brake block;

[0047] As a further improvement of the above scheme, the random stability control system further comprises:

[0048] a displacement sensor for collecting the brake pedal displacement ratio of the automobile at the initial braking; k

[0049] a speed sensor for collecting the driving speed of the automobile at the initial braking; v

[0050] a braking intention recognition module for providing the braking intention of the automobile according to v and k obtained based on a fuzzy algorithm; j different degrees of j represent different braking conditions;

[0051] The brake electronic control unit is further configured to determine j whether the braking condition belongs to the calibrated braking condition, and if yes, the brake motor is controlled in terms of braking pressure, otherwise, the brake motor is not controlled in terms of braking pressure.

[0052] The application also provides an automobile equipped with any of the above random stability control systems, for realizing the random stability control of the automobile during braking.

[0053] Compared with the prior art, the application is aimed at the mild braking condition (i.e. in the calibrated braking condition), and according to the wheel angular velocity ω and the braking pressure on the brake block F N at the initial braking of the automobile, the brake motor is controlled in terms of braking pressure, which effectively realizes the suppression of the automobile brake chatter and improves the automobile braking performance while ensuring the safety of the automobile braking.The application solves the technical problem that the conventional technology is difficult to effectively realize the suppression of the automobile brake chatter while ensuring the safety of the automobile braking.

[0054] The application can first collect the driving speed, the wheel angular velocity, the brake pedal displacement ratio and the braking pressure at the initial braking of the automobile, divide the braking intention into three braking modes, i.e. mild braking, moderate braking and emergency braking, then design a driver braking intention recognition method by using fuzzy reasoning rules, obtain the Lyapunov index of the automobile braking system based on the random dynamics theory for the mild braking condition, solve the stability condition of the automobile braking pressure, and regulate and control the electronic mechanical brake pressure based on the above, which effectively realizes the suppression of the automobile brake chatter and further improves the automobile braking performance while ensuring the safety of the automobile braking.

[0055] ​Therefore, the core advantages of the present invention are as follows:

[0056] 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).

[0057] 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

[0058] 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.

[0059] Figure 2 for Figure 1 Flowchart of the method for identifying vehicle braking intention adopted by the braking intention recognition module.

[0060] Figure 3 for Figure 2 Membership function diagram of vehicle speed, brake pedal displacement ratio, and vehicle braking intention used in the recognition method.

[0061] Figure 4 for Figure 2 Schematic diagram of the vehicle braking pattern inference results used in the recognition method.

[0062] Figure 5 for Figure 1 Flowchart of the stochastic stability control method employed by the central braking electronic control unit.

[0063] Figure 6 For application Figure 1 Schematic diagram of the rear stability boundary results of the electromechanical braking system.

[0064] Figure 7 For different brake pressures Figure 1 Energy probability density diagram of the vehicle braking system.

[0065] Figure 8 This is a flow chart of the random stability control method provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0066] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0067] It should be noted that when a component is referred to as being "mounted on" another component, it can be directly on the other component or there can be a middle component. When a component is referred to as being "disposed on" another component, it can be directly disposed on the other component or there can be a middle component. When a component is referred to as being "fixed on" another component, it can be directly fixed on the other component or there can be a middle component.

[0068] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0069] Embodiment 1

[0070] Please refer to Figure 1 which is a schematic diagram of the working principle of an electronic mechanical brake system of an automobile provided by the embodiments of the present application. The automobile comprises the electronic mechanical brake system, and further comprises a wheel 1, a flange plate 2, and a brake disc 3. The brake disc 3 is mounted on the wheel 1 through the flange plate 2, and when the brake disc 3 is driven or braked, the wheel 1 is also correspondingly driven or braked.

[0071] 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.

[0072] 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. 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 , 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.

[0073] 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 ω .

[0074] 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 2The shown automobile brake intention recognition method adopted by the brake intention recognition module 10 j may be: collecting v and k , querying a fuzzy rule table according to v and k to obtain j . The fuzzy rule table is a fuzzy inference rule established by taking v , k as input variables, taking j as an output variable, and fuzzifying v , k and j . The design method of the fuzzy rule table can include the following steps: fuzzification: dividing the input variables into multiple levels, dividing the output variables into multiple levels, and assigning the value range of each interval of the input variables and the output variables; fuzzy inference: establishing a fuzzy rule table for each interval value range; defuzzification: defuzzifying the fuzzy inference results in the fuzzy rule table to obtain the defuzzified automobile brake judder mode, which represents different brake working conditions.

[0075] In this embodiment, the brake intention recognition module 10 implements a driver brake intention recognition method based on fuzzy inference, taking the driving speed and brake pedal displacement ratio as inputs, and taking the automobile brake intention as output. The specific steps of fuzzy inference are as follows.

[0076] 1) Fuzzification

[0077] The input variables are divided into four levels {low, medium-low, medium-high, high}, and the fuzzy set corresponding to the input variables is {S, M, L, H}. The output variables are divided into three levels {mild, moderate, urgent}, and the fuzzy set corresponding to the output variables is {NL, NM, NH}. Of course, in other embodiments, the levels of the input variables can be more, and of course they can be two or three; similarly, the output variables can be two, and more.

[0078] The value range of each interval of the input variables is listed in Table 1.

[0079] Table 1 Fuzzy set of driving speed and brake pedal displacement ratio

[0080]

[0081] The value range of each interval of the output variables is listed in Table 2.

[0082] Table 2 Fuzzy set of brake intensity

[0083]

[0084] The trapezoid is used for the membership function of the driving speed, brake pedal displacement ratio, and automobile braking intention, and the mathematical expression is shown as equation (1), and the specific membership function of the embodiment of the application is shown as Figure 3 .

[0085] (1)

[0086] In the equation, u ( s ) is the membership function, s is the input variable, g 1, g 2, g 3, g 4 are respectively the left boundary, left vertex, right vertex, and right boundary of the trapezoidal membership function.

[0087] 2) Fuzzy inference

[0088] The fuzzy rule base is established. For the two input variables of driving speed and brake pedal displacement ratio, and the one output variable of automobile braking intention, 16 fuzzy rules are established as shown in Table 3. Figure 1

[0089] Table 3 Fuzzy rule table

[0090]

[0091] 3) Defuzzification

[0092] The defuzzification of the fuzzy inference result is processed by using the barycenter method, and the calculation formula is as follows:

[0093] (2)

[0094] In the equation, η 0 is the automobile braking vibration mode after defuzzification, x n is the output variable, μ ( x n ) is the output variable x n corresponding to the membership degree. The inference result of the automobile braking mode in the embodiment of the application can be seen from Figure 4 .

[0095] ​The braking intention recognition method of the braking intention recognition module 10 is not limited to the braking intention recognition method of the present application, and in other embodiments, the braking intention recognition module 10 can also not be provided, and the braking power electronic control unit 9 is directly given the intention of the calibrated braking condition (i.e. the light braking condition), and in the light braking condition, the braking power electronic control unit 9 is directly triggered to control the brake pressure of the brake motor 8. At this time, the displacement sensor 11 and the vehicle speed sensor 12 of the data acquisition unit can also not be provided.

[0096] Even in other embodiments, an unknown braking condition can be directly input to the braking power electronic control unit 9, and then the braking power electronic control unit 9 judges whether the unknown braking condition is a light braking condition, and in the light braking condition, the brake pressure of the brake motor 8 is controlled. For example, the electronic mechanical brake system random stability control method adopted by the braking power electronic control unit 9 can be used to judge j whether it belongs to the calibrated braking condition, and 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.

[0097] As Figure 5 shown, the brake pressure control here can be specifically: collecting the wheel angular velocity ω and the brake pressure on the brake block F N at the initial time of the automobile braking ω and F N calculating the target brake pressure output by the brake motor, and adjusting the brake pressure output by the brake motor to the brake block to .

[0098] Wherein, , in the formula, m , c 2 is the mass and damping of the brake block; p is the random control parameter of the brake disc torsional stiffness; D is the Gaussian white noise; k 1, I , c 1 is the torsional stiffness, moment of inertia and torsional damping of the brake disc; R is the equivalent action radius of the brake block acting on the brake disc; k 1 is the first-order fitting coefficient of the automobile braking system friction model; a is the attenuation coefficient; Δμ is an intermediate variable, and its formula is: 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.

[0099] 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.

[0100] 1) Two-degree-of-freedom stochastic dynamics modeling

[0101] Based on the wheel angular velocity signal at the initial stage of vehicle braking ω and brake pressure signal F N , a two-degree-of-freedom dynamic model considering the brake disc angle and brake pad displacement is established:

[0102] (3)

[0103] Where, θ 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; p is the random control parameter of the brake disc torsional stiffness; ξ ( 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:

[0104] (4)

[0105] Where, d 0 and Δμ They are all intermediate variables, and their formulas are: , Δμ = μs - μ k , μ k is the sliding friction coefficient between the brake disc and the brake pad, μ s is the static friction coefficient between the brake disc and the brake pad, a is the damping coefficient; d 1, d 2, d 3 are intermediate variables, whose formulas are respectively: , , , k 1 and k 2 are the linear term and the cubic term fitting coefficients of the automobile brake system friction model respectively.

[0106] Let , , formula (3) is converted to

[0107] (5)

[0108] In the formula, is the Hamilton function of the automobile brake system, m 11 , m 12 , m 21 , m 22 , σ 11 , δ 1, δ 2, k 11 , k 12 , k 13 , k 14 , k 21 , k 22 , k 23 are intermediate variables, whose formulas are respectively: , , , , ; , , , , , , , , , B t is a standard Weiner process.

[0109] 3) Analysis of the stochastic stability boundary

[0110] Since the automotive brake shimmy system is a quasi-nonintegrable Hamiltonian system, equation (5) can be transformed into an average Itô stochastic differential equation, i.e.

[0111] (6)

[0112] where, m H is a drift coefficient, is a diffusion coefficient. Then, linearizing equation (6) at H = 0, we have

[0113] (7)

[0114] where, , is a diffusion coefficient;

[0115] To ensure the stability of the automotive brake system, the maximum Lyapunov exponent of the average Itô stochastic differential equation is as follows:

[0116] (8)

[0117] When < 0, the trivial solution of the automotive brake system is asymptotically stable with a local probability of 1; when > 0, the trivial solution of the automotive brake system is asymptotically unstable with a local probability of 1; and when = 0, the automotive brake system is in a critical stable state.

[0118] Let < 0, the expression of the stochastic stability boundary of the brake pressure with respect to the wheel angular velocity can be solved as follows:

[0119] (9)

[0120] Obviously, since the reduction of the brake pressure under different wheel angular velocities helps to suppress the automotive shimmy phenomenon, but this method may increase the braking distance of the automobile. Therefore, based on the above-mentioned recognition of the driver's intention, only in the light braking condition, the brake stability control method proposed in the present application is triggered to ensure the safety of the automotive driving.

[0121] ​​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,

[0122] (10)

[0123] Where, .

[0124] Next, the effect of the present invention is verified and explained through a specific example data.

[0125] The parameters of the embodiment are I =0.65kg•m 2 , c 1=1N•m•s / rad, k 1=300000N•m / rad, R =0.12m, m =1.5kg, c 2=1N•s / m, k 2=300000N / m, μ s =0.5, μ k =0.3, p = 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 ω and brake pressure F N The stability boundary of Figure 6 shown.

[0126] by ω =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 pressureF N When the energy probability density of the automobile braking system is single-peak, the system response converges to the probability limit cycle, and the system steady solution is probability unstable. Apparently, the random stability control method of the electromechanical braking system is effective.

[0127] Embodiment 2

[0128] In the embodiment, a random stability control system is introduced, which is different from the random stability control system in Embodiment 1 in that the random stability control system in the embodiment comprises a controller in addition to the data acquisition unit in Embodiment 1. The controller is used to realize the functions of the braking intention recognition module 10 and the brake force electronic control unit 9 in Embodiment 1, and is an integration of the braking intention recognition module 10 and the brake force electronic control unit 9 in Embodiment 1.

[0129] As shown in Figure 8 , the random stability control method adopted by the controller in the embodiment comprises the following steps:

[0130] Collecting the driving speed of the automobile at the initial braking v , the displacement ratio of the brake pedal k , the wheel angular velocity ω , and the brake pressure on the brake block F N ;

[0131] According to v and k , the braking intention of the automobile is obtained based on the fuzzy algorithm j , and different degrees of braking working conditions are represented by different degrees of braking intention j ;

[0132] Judging whether it belongs to the calibrated braking working condition, and if yes, the brake motor is controlled for brake pressure, otherwise, the brake motor does not need to be controlled for brake pressure j ;

[0133] According to ω and F N , the target brake pressure output by the brake motor is calculated ;

[0134] The brake pressure output by the brake motor to the brake block is adjusted to .

[0135] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present disclosure.

[0136] The random stability control system of the present application can realize functional renewal of old cars, and increase the random stability control function in the brake control function, and 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 ω 0 A displacement sensor 11 is installed to collect the displacement ratio of the brake pedal at the initial braking of the car k A speed sensor 12 is installed to collect the driving speed of the car at the initial braking of the car v A pressure sensor 13 is installed to collect the brake pressure on the brake block at the initial braking of the car F N An angular velocity sensor 14 is installed to collect the wheel angular velocity at the initial braking of the car ω Finally, a brake force electronic control unit 9 and a brake intention recognition module 10 are installed in the car to be linked with the brake motor 8 of the old car, so that the old car can be functionally renewed, and of course, the brake force electronic control unit 9 and the brake intention recognition module 10 can not be installed, and the functions of the brake force electronic control unit 9 and the brake intention recognition module 10 can be designed as software, so that the software patch of the vehicle-mounted system of the old car is completed, and the functions of the brake force electronic control unit 9 and the brake intention recognition module 10 are completed through the updated vehicle-mounted system.

[0137] The new car designed with the functions of the present application can also be manufactured, and the displacement sensor 11, the speed sensor 12, the pressure sensor 13, and the angular velocity sensor 14 are installed in the new car at the factory, and the vehicle-mounted system of the new car itself can complete the functions of the brake force electronic control unit 9 and the brake intention recognition module 10, and the consumer does not need to purchase the random stability control system separately.

[0138] Therefore, whether it is a new car or an old car, the technology of the present application can be well applied, and the present application is easy to popularize and apply, and has high technical transformation value and commercial prospect.

[0139] The above-described embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it cannot be understood as a limitation on the scope of the application. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A random stability control method for 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 initiates braking 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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