A method for assessing and warning of instability risk in automotive braking systems and its automotive applications

Abstract

This invention belongs to the field of automotive braking technology, specifically relating to a method for assessing and warning of instability risk in automotive braking systems and a corresponding automotive vehicle. The method includes: calculating characteristic values ​​during vehicle braking. l t ; Calculate the proportion of flutter energy during vehicle braking or t ;according to l t and or t The precise probability of vehicle flutter during braking is obtained based on a fuzzy algorithm. P t ;judge P t Is it greater than or equal to the calibrated probability? P 0; when P t ≥ P At time 0, an early warning is issued regarding the risk of vehicle braking chatter instability; otherwise, no warning is required. The core advantages of this invention are: 1) It theoretically solves for real-time characteristic values ​​that characterize the stability of the vehicle braking system, while simultaneously collecting brake disc angular velocity signals to calculate the proportion of chatter energy; 2) It establishes fuzzy inference rules that integrate the mechanism model and measured data to assess the risk of vehicle braking chatter instability, providing technical support for the accurate prediction and control of vehicle braking chatter in engineering.

Description

Technical Field

[0001] This invention relates to a risk assessment and early warning method in the field of automotive braking technology, specifically to a method for assessing and warning of automotive braking system instability risk, an automotive braking control method using the method, an automotive braking system instability risk assessment and early warning system using the method, and an automotive braking system instability risk assessment and early warning system using the method, as well as an automotive vehicle using the system. Background Technology

[0002] Brake shudder is a vibration of the brake disc caused by instability in the braking system during braking. It leads to uneven contact between the brake disc and friction pads, accelerating brake disc wear, severely reducing braking efficiency, increasing braking distance, and jeopardizing driving safety. Simultaneously, this vibration is transmitted from the braking system to the brake pedal, steering wheel, and vehicle body, affecting ride comfort and reducing the vehicle's dynamic performance.

[0003] Existing technologies primarily predict or identify brake shudder in automobiles by collecting signals from relevant sensors such as the master cylinder hydraulic sensor and the brake disc angular velocity signal. For example, ZL202010690175.5, "System and Method for Detecting Brake Shudder in Vehicles," determines whether brake shudder has occurred by judging the frequency and amplitude of signals detected from the steering wheel torque sensor, acceleration sensor, and master cylinder hydraulic sensor. Another example is ZL202211340883.1, "An Electric Wheel Brake Shudder Prediction and Control System and Method Thereof," which collects brake disc angular velocity signals and wheel hub motor angular velocity signals, and identifies shudder based on the obtained desired braking pressure signal and brake disc angular velocity signal.

[0004] The above methods all rely too heavily on vibration signals from the vehicle's braking system, making them highly susceptible to external disturbances and system parameter perturbations. Furthermore, purely theoretical analysis can deviate significantly from actual conditions, resulting in poor accuracy in assessing the risk of vehicle brake flutter instability. Summary of the Invention

[0005] To address the technical problem of poor accuracy caused by the use of a single indicator in existing vehicle brake flutter instability risk assessments, this invention provides a method for assessing and warning of vehicle brake system instability risk, a vehicle brake control method using the aforementioned method, a vehicle brake system instability risk assessment and warning system using the aforementioned method, and a vehicle using the aforementioned vehicle brake system instability risk assessment and warning system.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: a method for assessing and warning of instability risk in an automotive braking system, comprising the following steps:

[0007] Based on the wheel angular velocity during vehicle braking oh Braking pressure on the brake disc F N Calculate the characteristic values ​​of the vehicle braking system l t :

[0008] ;

[0009] In the formula, r It is a random control parameter for the torsional stiffness of the brake disc; D It is the intensity of Gaussian white noise; I r , k r , c r These are the moment of inertia, torsional stiffness, and torsional damping of the brake disc, respectively. k 1 is the coefficient of fit for the first-order term of the friction model of the automotive braking system. a It is the attenuation coefficient, Δ m It is an intermediate variable: Δ m = m s - m k , m k , m s These are the coefficient of sliding friction and the coefficient of static friction between the brake disc and the brake pads, respectively. R It is the equivalent radius of action of the brake pads on the brake disc. m b , c b These are the mass and damping of the brake pad, respectively.

[0010] Based on the angular velocity of the brake disc during vehicle braking Calculate the percentage of flutter energy during vehicle braking. or t ;

[0011] according to l t and or t The probability of vehicle brake flutter phenomenon is obtained based on fuzzy algorithm. s t ,and s t The precise value is obtained by defuzzification. P t ;

[0012] judge Pt Is it greater than or equal to the calibrated probability? P 0; when P t ≥ P At 0:00, a warning is issued for the risk of vehicle braking vibration and instability; otherwise, no warning is required.

[0013] As a further improvement to the above scheme, l t The design methodology includes the following steps:

[0014] Establish containing and F N A two-degree-of-freedom stochastic dynamic model of automotive braking flutter:

[0015] (1)

[0016] In the formula, x、 , These are the displacement, velocity, and acceleration of the brake block, respectively. i r , These are the rotation angle and acceleration of the brake disc, respectively. x ( t ) is a strength of 2 D Gaussian white noise; F μ It is the braking friction force acting on the brake disc; k b It refers to the stiffness of the brake pads;

[0017] Will F μ Designed as follows:

[0018] (3)

[0019] In the formula, d 0、 d 1. d 2. d 3 are all intermediate variables: ; ; ; ; k 2 represents the coefficients of the cubic term in the friction model fitting of the automotive braking system;

[0020] make , And take the Hamiltonian function as Equation (1) can be transformed into:

[0021] (4)

[0022] m 11 , m 12 , m 21 , m 22 , s 11 , d 1. d 2. k 12 , k 14 , k 21 , k 22 These are all intermediate variables: , , , , ; , , , , , , B ( t This is a standard Weiner process;

[0023] The characteristic value solution, based on stochastic dynamics theory, is expressed by equation (4) as a one-dimensional stochastic process, i.e.

[0024] (5)

[0025] In the formula, m ( H ) is the drift coefficient. s ( H ) is the diffusion coefficient. H The Hamiltonian value is expressed as follows:

[0026] (6)

[0027] In the formula, It is about H Second order infinitesimal;

[0028] Based on the first type of singularity boundary theory of one-dimensional stochastic processes, the characteristic scaling value characterizing the stability of the automobile braking system is... l t for:

[0029] (7)

[0030] intermediate variables k 11, k 13 , k 23 Substituting into equation (7), we get:

[0031] .

[0032] As a further improvement to the above scheme, or t for:

[0033] ;

[0034] In the formula, It is the angular velocity of the brake disc. In the power spectrum, the amplitude corresponding to the braking flutter instability frequency, It is the angular velocity of the brake disc. The sum of the amplitudes of all frequencies in the power spectrum.

[0035] Furthermore, the calibrated flutter instability frequency range is 20 Hz to 30 Hz.

[0036] As a further improvement to the above scheme, P t for:

[0037] ;

[0038] In the formula, m ( s t )yes s t membership function, A yes s t The lower limit, B yes s t The upper limit.

[0039] Furthermore, precise value P t The design methodology includes the following steps:

[0040] according to l t and or t Querying the fuzzy rule table yields s t Fuzzy rule tables are used to... l t and or t As input variables, s t Fuzzy inference rules established as output variables;

[0041] Defuzzing s t get P t .

[0042] Furthermore, the design method for fuzzy rule tables includes the following steps:

[0043] Fuzzification: Divide the input variable into multiple levels, divide the output variable into multiple levels, and assign a range of values ​​to each interval of the input and output variables;

[0044] Fuzzy reasoning: Establish a fuzzy rule table for the value range of each interval;

[0045] Defuzzification: Defuzzify the fuzzy inference results in the fuzzy rule table to obtain the precise value.

[0046] The present invention also provides a vehicle braking control method, which includes the following steps:

[0047] Control the brake blocks to apply braking force;

[0048] Meanwhile, using the aforementioned methods for assessing and warning of instability risks in any automotive braking system, we can evaluate whether it is necessary to issue a warning about the risk of automotive brake flutter instability during braking.

[0049] This invention also provides a vehicle braking system instability risk assessment and early warning system, comprising:

[0050] Angular velocity sensor 1 is used to collect the wheel angular velocity during vehicle braking. oh ;

[0051] Brake pressure sensor is used to collect brake pressure during vehicle braking. F N ;

[0052] Angular velocity sensor two is used to collect the angular velocity of the brake disc during vehicle braking. ;

[0053] Data processor, used for... oh , F N and Using the above-mentioned methods for assessing and warning of instability risks in any automotive braking system, we can evaluate whether it is necessary to issue a warning about the risk of automotive brake flutter instability during braking.

[0054] The present invention also provides a car equipped with the aforementioned car braking system instability risk assessment and early warning system, which assesses whether it is necessary to warn of the risk of car braking flutter instability during car braking.

[0055] This invention primarily establishes a two-degree-of-freedom stochastic dynamic model of automotive brake flutter considering the uncertainty of brake disc torsional stiffness. It theoretically solves for characteristic values ​​characterizing the stability of the automotive braking system, and calculates the proportion of flutter energy based on brake disc angular velocity signals. A fuzzy inference rule integrating the mechanistic model and measured data is established to design an automotive brake flutter instability risk assessment module, providing accurate early warnings for automotive braking system tuning. This invention effectively solves the problems of existing technologies: "Existing inventions mainly predict or identify automotive brake flutter by collecting signals from the master cylinder hydraulic sensor and brake disc angular velocity signals. These methods rely too heavily on the vibration signals of the automotive braking system and are easily affected by external disturbances and system parameter perturbations. Furthermore, purely theoretical analysis can deviate significantly from actual conditions, resulting in poor accuracy in assessing the risk of automotive brake flutter instability." In other words, it solves the technical problem of poor accuracy in existing automotive brake flutter instability risk assessments.

[0056] The core advantages of the method described in this invention are: 1) A two-degree-of-freedom stochastic dynamic model of vehicle brake flutter considering the uncertainty of brake disc torsional stiffness is established, the characteristic values ​​characterizing the stability of the vehicle braking system are theoretically solved, and the brake disc angular velocity signal is collected to calculate the proportion of flutter energy; 2) A fuzzy inference rule that integrates the mechanism model and measured data is established, and a vehicle brake flutter instability risk assessment module is designed based on this rule, providing technical support for the accurate prediction and control of vehicle brake flutter in engineering. Attached Figure Description

[0057] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0058] Figure 1 This is a schematic diagram of the structure of a car provided in an embodiment of the present invention.

[0059] Figure 2 for Figure 1 Flowchart of the method for assessing and warning of instability risks in automotive braking systems adopted by China Automotive.

[0060] Figure 3 yes Figure 2 The method for assessing and warning of instability risk in automotive braking systems uses system characteristic values, flutter energy ratio, and membership function graphs of flutter occurrence probability.

[0061] Figure 4 Is adopted Figure 3 A schematic diagram illustrating the results of an embodiment of a method for assessing and warning of instability risks in automotive braking systems. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0063] Please see Figure 1 , Figure 1 This is a schematic diagram of an automobile structure disclosed in an embodiment of the present invention. The automobile includes a wheel 1, a brake disc 2 for braking the wheel 1, a brake block 3 for braking the brake disc 2, and an automobile braking control system for driving the brake block 3 to brake.

[0064] The automotive braking control system includes a brake motor (not shown) that drives the brake pads 3 for braking, a controller (not shown) that controls the operation of the brake motor, and an automotive braking system instability risk assessment and early warning system that evaluates whether a warning is needed for the risk of brake flutter instability during braking. The braking control method of the automotive braking control system is scheduled and operated by the controller, and includes the following steps: controlling the brake pads to brake (i.e., controlling the brake motor to drive the brake pads 3 to brake the brake disc 2); simultaneously, activating the automotive braking system instability risk assessment and early warning system to evaluate whether a warning is needed for the risk of brake flutter instability during braking.

[0065] The vehicle braking system instability risk assessment and early warning system includes a wheel angular velocity sensor for collecting data from wheel 1 during vehicle braking. oh Angular velocity sensor 4 is used to collect the angular velocity of brake disc 2 during vehicle braking. The angular velocity sensor 26 is used to collect the braking pressure of the brake pad 3 when the car is braking. F N Pressure sensor 5, according to oh , F N and The system includes an instability risk assessment module 7, which evaluates the risk of vehicle brake shudder instability during braking, and an early warning module 8, which issues a warning when the instability risk assessment module 7 detects the presence of such a risk. The vehicle braking system instability risk assessment and early warning method mainly includes the following steps: collecting wheel angular velocities during vehicle braking. oh Collect braking pressure during vehicle braking. F N Angular velocity of the brake disc during vehicle braking ;according to oh , F N and The system assesses whether there is a risk of brake shudder instability during vehicle braking and issues a warning if so. The software functions corresponding to the instability risk assessment module 7 and the warning module 8 can be loaded into the controller of the vehicle braking control system, thus requiring only one controller in terms of hardware.

[0066] Please refer to section 2 for the detailed flowchart of the method for assessing and warning of instability risks in automotive braking systems. The method includes steps one through five.

[0067] 1. Collect the wheel angular velocity during vehicle braking oh Collect braking pressure during vehicle braking F N Collect the angular velocity of the brake disc during vehicle braking. .

[0068] As described above, this step can be performed by angular velocity sensor 4, pressure sensor 5, and angular velocity sensor 6. oh It is the initial angular velocity of the wheel during braking, and It is the angular velocity response vibration of the brake disc.

[0069] II. Based on the wheel angular velocity during vehicle braking oh Braking pressure on the brake disc F N Calculate the characteristic values ​​of the vehicle braking system l t .

[0070] l t for:

[0071] In the formula, r It is a random control parameter for the torsional stiffness of the brake disc;

[0072] D It is the intensity of Gaussian white noise;

[0073] I r It is the moment of inertia of the car's brake disc; k 11 , k 13 , k 23 All are intermediate variables: , , ; k r It refers to the torsional stiffness of the car's brake disc. m b It is the mass of the car's brake pads. RIt is the equivalent radius of action of the brake pads on the brake disc. c r It is the brake disc torsional damping. c b Brake block damping; d 1 is an intermediate variable: In the formula, k 1 represents the coefficient of the first-order term in the friction model fitting of the automotive braking system. a It is the attenuation coefficient, Δ m It is an intermediate variable: Δ m = m s - m k , m k It is the coefficient of sliding friction between the brake disc and the brake pads. m s It is the coefficient of static friction between the brake disc and the brake pads.

[0074] If we take the intermediate variable k 11 , k 13 , k 23 Substitute ,available:

[0075]

[0076] when l t < At 1 o'clock, it was determined that "the probability of the vehicle braking vibration phenomenon occurring is extremely small". l t When =1, it is determined that "the vehicle brake vibration system is in a critical state of random stability". l t > At time 1, it was determined that "the probability of vehicle brake vibration is relatively high".

[0077] Existing technologies primarily predict or identify vehicle brake chatter by collecting signals such as master cylinder hydraulic sensors and brake disc angular velocity. These methods are all based on deterministic theory and neglect the influence of perturbations in the vehicle braking system parameters. This invention, however, establishes a two-degree-of-freedom stochastic dynamic model of vehicle brake chatter that considers the uncertainty of brake disc torsional stiffness. It uses stochastic stability theory to solve for characteristic values ​​representing the stability of the vehicle braking system, which are then used as input to the vehicle brake chatter instability risk assessment module. Therefore, the following section... l t The design process will be explained in detail with reasoning.

[0078] Collect the angular velocity of the brake disc during vehicle braking Braking pressure on the brake disc F N Noise signals are removed by filtering to establish a system that includes the brake disc rotation angle. i r and brake block displacement x Two-degree-of-freedom dynamic model:

[0079] (1)

[0080] In the formula, i r It is the rotation angle of the brake disc. It is the angular velocity of the brake disc. It is the acceleration of the brake disc;

[0081] x It is the displacement of the brake block. It is the speed of the brake block. It is the acceleration of the brake block;

[0082] I r It is the moment of inertia of the brake disc;

[0083] c r It is the torsional damping of the brake disc;

[0084] k r It refers to the torsional stiffness of the brake disc;

[0085] m b It is the mass of the brake block;

[0086] c b It is brake pad damping;

[0087] k b It refers to the stiffness of the brake pads;

[0088] r It is a random control parameter for the torsional stiffness of the brake disc;

[0089] x ( t ) is a strength of 2 D Gaussian white noise;

[0090] R It is the equivalent radius of action of the brake pads on the brake disc;

[0091] F μ It is the braking friction force experienced by the brake disc.

[0092] Among them, based on the Stribeck friction effect, F μ The exponential form of is expressed as follows.

[0093] (2)

[0094] In the formula, m k It is the coefficient of sliding friction between the brake disc and the brake pads;

[0095] m s It is the coefficient of static friction between the brake disc and the brake pads;

[0096] a It is the attenuation coefficient;

[0097] It is the linear velocity of the brake disc relative to the brake pads.

[0098] For ease of theoretical analysis, equation (2) is simplified as follows:

[0099] (3)

[0100] In the formula, Δ m It is an intermediate variable: Δ m = m s - m k ;

[0101] d 0、 d 1. d 2. d 3 is an intermediate variable: , , , , k 1 and k 2 represents the coefficients of the first-order and third-order terms in the friction model fitting.

[0102] make , And take the Hamiltonian function as Equation (1) can be transformed into

[0103] (4)

[0104] In the formula, 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 These are all intermediate variables: , , , , ; , , , , , , , , ;

[0105] B ( t This is a standard Weiner process;

[0106] The characteristic value solution, based on stochastic dynamics theory, can be expressed as a one-dimensional stochastic process, i.e.

[0107] (5)

[0108] In the formula, m ( H ) is the drift coefficient. s ( H ) is the diffusion coefficient. H The Hamiltonian value is expressed as follows:

[0109] (6)

[0110] In the formula, It is about H Second order infinitesimal.

[0111] According to the first-kind singularity boundary theory of one-dimensional stochastic processes, the characteristic scale characterizing the stability of a car braking system is...

[0112] (7)

[0113] If we take the intermediate variable k 11 , k 13 , k 23 Substituting into equation (7), we get:

[0114]

[0115] when l t < At time 1, the equilibrium solution of the vehicle brake shudder system is stochastically stable, and the probability of vehicle brake shudder occurring is extremely small. l t = At time 1, the vehicle's brake vibration system is in a critical state of random stability and may become randomly unstable. l t > At time 1, the equilibrium solution of the vehicle brake shudder system is random and unstable, and the probability of vehicle brake shudder occurring is relatively high.

[0116] III. Based on the angular velocity of the brake disc during vehicle braking Calculate the percentage of flutter energy during vehicle braking. or t .

[0117] Based on the angular velocity of the brake disc Solve for the angular velocity of the brake disc. The power spectrum is used to calculate the proportion of energy involved in vehicle braking flutter. or t :

[0118]

[0119] in, It is the angular velocity of the brake disc. In the power spectrum, the amplitude corresponding to the braking flutter instability frequency, It is the angular velocity of the brake disc. The sum of the amplitudes of all frequencies in the power spectrum.

[0120] The calibrable flutter instability frequency range is 20 Hz to 30 Hz. or t ≥ or At 0:00, it is determined that "vehicle brake vibration phenomenon has occurred". or t <the At 0:00, it was determined that "no vehicle brake vibration phenomenon occurred"; or 0 is the calibrated energy percentage threshold.

[0121] IV. According to l t and or t The probability of vehicle brake flutter phenomenon is obtained based on fuzzy algorithm. s t ,and s t The precise value obtained by defuzzification P t .

[0122] according to l t and or t The probability of real-time vehicle braking vibration is obtained by querying the fuzzy rule table. s t Fuzzy rule tables are used to... l t , or t As input variables, s t As an output variable, fuzzification processing l t , or t and s t The established fuzzy reasoning rules.

[0123] As stated above, the essence of this invention is: "This invention mainly establishes a two-degree-of-freedom stochastic dynamic model of automotive brake flutter considering the uncertainty of brake disc torsional stiffness, theoretically solves the characteristic values ​​characterizing the stability of the automotive braking system, calculates the flutter energy ratio based on the brake disc angular velocity signal, establishes fuzzy inference rules that integrate the mechanism model and measured data, and designs an automotive brake flutter instability risk assessment module based on this, thus providing accurate early warning for automotive braking system tuning." Therefore, l t , or t As input variables, s t It is important to formulate fuzzy inference rules as output variables. Therefore, although fuzzy inference rules are common, they are not used in automobiles. l t , or t As input variables, s t The idea of ​​using output variables to formulate fuzzy inference rules is innovative. Therefore, this paper discusses the concept of using output variables to formulate fuzzy inference rules. l t , or t , s tThe rules of fuzzy reasoning will be explained in detail.

[0124] To improve the accuracy and reliability of instability risk assessment for automotive braking systems, this invention proposes an instability risk assessment method for automotive braking systems that integrates mechanistic models and measured data, based on fuzzy reasoning. The method uses characteristic values ​​and the proportion of flutter energy as inputs and the probability of automotive braking flutter as output. The specific steps are as follows.

[0125] 1) Blurring process

[0126] The input and output variables are divided into five levels: {negative large, negative small, zero, positive small, positive large}. Therefore, the fuzzy sets of the input and output variables can be defined as {NB, NS, O, PS, PB}, where NB represents negative large, NS represents negative small, O represents zero, PS represents positive small, and PB represents positive large. The value ranges of the input and output variables in each interval are shown in Table 1 below.

[0127] Table 1. Fuzzy sets of characteristic values, flutter energy percentage, and flutter occurrence probability.

[0128]

[0129] The membership functions for characteristic values, flutter energy percentage, and flutter occurrence probability are triangular, and their mathematical expressions are shown in equation (8). The specific membership function in this invention example is as follows: Figure 3 As shown.

[0130] (8)

[0131] In the formula, u ( s ) is a membership function. s Are they input or output variables? , , These are the left boundary, vertex, and right boundary of the membership function of the triangle, respectively.

[0132] 2) Fuzzy reasoning

[0133] A fuzzy rule base was established. Twenty-five fuzzy rules were established for the two input quantities (feature label and flutter energy percentage) and the one output quantity (flutter probability), as shown in Table 2 below.

[0134] Table 2 Fuzzy Rule Base

[0135]

[0136] 3) Defuzzification of the probability of vehicle brake flutter. s t Get precise value P t .

[0137] The centroid method is used to defuzzify the above fuzzy inference results. The calculation formula is as follows:

[0138] (9)

[0139] In the formula, m ( s t )yes s t membership function, A yes s t The lower limit, B yes s t The upper limit.

[0140] V. Judgment P t Is it greater than or equal to the calibrated probability? P 0; when P t ≥ P At 0:00, a warning is issued regarding the risk of vehicle braking vibration and instability; otherwise, no warning is needed. Therefore, when P t ≥ P At 0:00, an early warning will be issued to promptly adjust the vehicle's braking system to prevent vibration. Otherwise, no early warning is needed for adjusting the vehicle's braking system.

[0141] Instability risk assessment module 7, based on the data processing in steps two through four, determines the probability of vehicle braking vibration occurring. P t ≥ P 0 ( P When the value of 0 is calibrated, an assessment indicates a risk of vehicle brake shudder instability. The warning module 8 issues a warning when the instability risk assessment module 7 determines there is a risk of vehicle brake shudder instability, reminding drivers and passengers to adjust the vehicle's braking system promptly to prevent shuddering.

[0142] To demonstrate the feasibility of this invention, data support is provided; please refer to [link / reference needed]. Figure 4 The parameters for the example are as follows: I r =0.65kg•m 2 , c r =1 N•m•s / rad, k r =300000 N•m / rad, R =0.12m, m b =2kg, cb =1N•s / m, k b =300000N / m m s =0.5, m k =0.3, r = 1720 N•m / rad, k 1 = -0.8598 s / m k 2 = 0.2638s 3 / m 3 , a = 4.6, D =0.15s, P 0 = 60%.

[0143] When collecting the initial angular velocity of the brake disc during vehicle braking oh =12 rad / s, braking pressure on the brake disc F N When the value is 1000N, the characteristic scale value of the automotive braking system can be calculated as follows: l t =1.04>1. Although the equilibrium solution of the automotive braking vibration system is random and unstable, it is close to the critical instability state, and the risk of system instability is difficult to assess theoretically.

[0144] Therefore, this invention can further infer the probability of vehicle flutter occurrence by combining the proportion of system flutter energy. For example, if the proportion of flutter energy in the vehicle's cornering vibration signal is 20%, the probability of vehicle braking flutter occurrence is 30.31%, which is less than... P 0% = 60%, indicating a low risk of instability in the vehicle's braking system; therefore, no braking system adjustment or warning is necessary. However, if the flutter energy proportion of the vehicle's cornering vibration signal is 50%, combined with the proportion of possible flutter energy, the probability of vehicle flutter occurring is 70.93%, which is greater than... P At a rate of 0% (60%), the risk of instability in the vehicle's braking system is relatively high. The vehicle braking system adjustment and warning module alerts the driver and passengers, adjusting the braking system to eliminate brake shudder. Clearly, the vehicle braking system instability risk assessment proposed in this invention integrates a fusion mechanism model and measured data, significantly improving the accuracy of instability risk assessment.

[0145] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing and warning of instability risk in an automotive braking system, characterized in that, It includes the following steps: Based on the wheel angular velocity during vehicle braking ω Braking pressure on the brake disc F N Calculate the characteristic values ​​of the automotive braking system λ t : ； In the formula, ρ It is a random control parameter for the torsional stiffness of the brake disc; D It is the intensity of Gaussian white noise; I r , k r , c r These are the moment of inertia, torsional stiffness, and torsional damping of the brake disc, respectively. κ 1 represents the coefficient of the first-order term in the friction model fitting of the automotive braking system. a It is the attenuation coefficient, Δ μ It is an intermediate variable: Δ μ = μ s - μ k , μ k , μ s These are the coefficient of sliding friction and the coefficient of static friction between the brake disc and the brake pads, respectively. R It is the equivalent radius of action of the brake pads on the brake disc. m b , c b These are the mass and damping of the brake pad, respectively. Based on the angular velocity of the brake disc during vehicle braking Calculate the percentage of flutter energy during vehicle braking. η t ; according to λ t and η t The probability of vehicle brake flutter phenomenon is obtained based on fuzzy algorithm. σ t ,and σ t The precise value is obtained by defuzzification. P t ; judge P t Is it greater than or equal to the calibrated probability? P 0; when P t ≥ P At 0:00, a warning is issued for the risk of vehicle braking vibration and instability; otherwise, no warning is required.

2. The method for assessing and warning of instability risk in automotive braking systems as described in claim 1, characterized in that, λ t The design methodology includes the following steps: Establish containing and F N A two-degree-of-freedom stochastic dynamic model of automotive braking flutter: （1） In the formula, x、 , These are the displacement, velocity, and acceleration of the brake block, respectively. θ r , These are the rotation angle and acceleration of the brake disc, respectively. ξ ( t ) is a strength of 2 D Gaussian white noise; F μ It is the braking friction force acting on the brake disc; k b It refers to the stiffness of the brake pads; F μ Designed as follows: （3） In the formula, d 0、 d 1. d 2. d 3 are all intermediate variables: ; ; ; ; κ 2 represents the coefficients of the cubic term in the friction model fitting of the automotive braking system; make , And take the Hamiltonian function as Equation (1) can be transformed into: （4） 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 These are all intermediate variables: , , , , ; , , , , , , , , ; B ( t This is a standard Weiner process; The characteristic value solution, based on stochastic dynamics theory, is expressed by equation (4) as a one-dimensional stochastic process, i.e. （5） In the formula, m ( H ) is the drift coefficient. σ ( H ) is the diffusion coefficient. H The Hamiltonian value is expressed as follows: （6） In the formula, It is about H Second order infinitesimal; Based on the first type of singularity boundary theory of one-dimensional stochastic processes, the characteristic scaling value characterizing the stability of the automobile braking system is... λ t for: （7） intermediate variables k 11 , k 13 , k 23 Substituting into equation (7), we get: 。 3. The method for assessing and warning of instability risk in automotive braking systems as described in claim 1, characterized in that, η t for: ； In the formula, It is the angular velocity of the brake disc. In the power spectrum, the amplitude corresponding to the braking flutter instability frequency, It is the angular velocity of the brake disc. The sum of the amplitudes of all frequencies in the power spectrum.

4. The method for assessing and warning of instability risk in automotive braking systems as described in claim 3, characterized in that, The calibrated flutter instability frequency range is 20 Hz to 30 Hz.

5. The method for assessing and warning of instability risk in automotive braking systems as described in claim 1, characterized in that, P t for: ； In the formula, μ ( σ t )yes σ t membership function, A yes σ t The lower limit, B yes σ t The upper limit.

6. The method for assessing and warning of instability risk in automotive braking systems as described in claim 5, characterized in that, Precision value P t The design methodology includes the following steps: according to λ t and η t Querying the fuzzy rule table yields σ t Fuzzy rule tables are used to... λ t and η t As input variables, σ t Fuzzy inference rules established as output variables; Defuzzing σ t get P t .

7. The method for assessing and warning of instability risk in automotive braking systems as described in claim 6, characterized in that, The design method for fuzzy rule tables includes the following steps: Fuzzification: Divide the input variable into multiple levels, divide the output variable into multiple levels, and assign a range of values ​​to each interval of the input and output variables; Fuzzy reasoning: Establish a fuzzy rule table for the value range of each interval; Defuzzification: Defuzzify the fuzzy inference results in the fuzzy rule table to obtain the precise value.

8. A method for controlling vehicle braking, characterized in that, It includes the following steps: Control the brake blocks to apply braking force; Meanwhile, the method for assessing and warning of instability risk of automotive braking system as described in any one of claims 1 to 7 is used to assess whether it is necessary to warn of the risk of automotive braking chatter instability during vehicle braking.

9. A system for assessing and warning of instability risk in an automotive braking system, characterized in that, It includes: Angular velocity sensor 1 is used to collect the wheel angular velocity during vehicle braking. ω ; Angular velocity sensor two is used to collect the angular velocity of the brake disc during vehicle braking. ; Brake pressure sensor is used to collect brake pressure during vehicle braking. F N ; Data processor, used for... ω , F N and The method for assessing and warning of instability risk of automotive braking system as described in any one of claims 1 to 7 is used to assess whether it is necessary to warn of the risk of automotive brake flutter instability during braking.

10. A car, characterized in that, The system is equipped with a vehicle braking system instability risk assessment and early warning system as described in claim 9, which assesses whether an early warning is needed for vehicle braking flutter instability risk during vehicle braking.

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