Brake stick-slip vibration active avoidance method and device based on braking torque distribution

Abstract

The invention relates to a method and a device for actively avoiding stick-slip vibration of a brake based on braking torque distribution, and compared with traditional passive methods such as structure matching, friction material selection and surface treatment, the method is an active suppression method. By adjusting the regenerative braking torque and the friction braking torque of the hybrid braking system, active avoidance of stick-slip vibration of the brake can be achieved, the method has good adaptability to changes of the working environment, the mechanical structure of the hybrid braking system does not need to be transformed, only the algorithm is improved, and therefore extra hardware cost is not increased, and the method is suitable for large-scale popularization and application. And a good stick-slip vibration active suppression effect can be obtained by regularly training and updating the neural network model.

Description

Technical Field

[0001] The present invention relates to the technical field of brakes, and in particular to a method and device for actively avoiding stick-slip vibration of brakes based on braking torque distribution. Background Art

[0002] The stick-slip vibration of brakes is one of the main sources of vibration and noise in vehicles. Especially in pure electric vehicles, due to the lack of the masking effect of the engine, the vibration and noise problems caused by the stick-slip vibration of brakes are more prominent. The suppression method of stick-slip vibration of automobile brakes has always been a frontier bottleneck problem in the field of automotive engineering. The current suppression methods for stick-slip vibration of brakes mainly include structural optimization matching, selection of friction materials and surface treatment of friction interfaces, but these methods are passive suppression methods and cannot actively adapt to changes in working conditions and other conditions. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a method and device for actively avoiding stick-slip vibration of a brake based on braking torque distribution.

[0004] The objective of the present invention is achieved through the following technical solutions:

[0005] In a first aspect, a brake stick-slip vibration active avoidance method based on braking torque distribution is provided, the active avoidance method comprising the following steps:

[0006] Based on a multi-body dynamics model described by a set of ordinary differential equations, a brake friction vibration model is constructed, wherein the multi-body dynamics model involves brake pad vibration and brake disc torsional vibration;

[0007] sequentially identifying and optimizing the parameters of the brake friction vibration model by measuring vehicle braking data and braking vibration data;

[0008] Based on the brake friction vibration model after parameter optimization, the stick-slip vibration boundary of the brake is obtained through nonlinear dynamics theory.

[0009] The stick-slip vibration boundary of the brake is set as a constraint condition, and the regenerative braking energy recovery efficiency is optimized based on typical cycle conditions with the goal of maximizing the regenerative braking energy recovery efficiency. This is used as input data to train the neural network model.

[0010] The regenerative braking energy recovery efficiency is predicted through the trained neural network model, and then the braking torque distribution coefficient of the hybrid braking system that can take into account the stick-slip vibration suppression of the brake is calculated, and the braking torque is distributed according to the braking torque distribution coefficient.

[0011] In some embodiments, sequentially identifying and optimizing the parameters of the brake friction vibration model by measuring vehicle braking data and vibration data includes:

[0012] Step S21, obtaining vehicle braking data and braking vibration data, and pre-processing the vehicle braking data and braking vibration data for identification of brake friction vibration model parameters;

[0013] Step S22, calculating the amplitude, fundamental frequency and energy of the braking vibration based on the braking vibration data in step S21;

[0014] The vehicle braking data in step S21 is used as input data and input into the brake friction vibration model that has been parameter-identified to calculate the amplitude, fundamental frequency and energy of the vehicle;

[0015] Step S23, respectively subtracting the two sets of amplitude, fundamental frequency and energy in step S22, and constructing a multi-objective weighted function including amplitude, fundamental frequency and energy with unknown parameters of the brake friction vibration model as variables;

[0016] Step S24, based on the multi-objective weighted function in step S23, the optimal values ​​of the unknown parameters of the brake friction vibration model are obtained through a multi-objective particle swarm optimization algorithm.

[0017] In some embodiments, based on the brake friction vibration model with optimized parameters, the brake stick-slip vibration boundary is obtained through nonlinear dynamics theory, including:

[0018] Step 31, setting parameters, deriving the subcritical Hopf bifurcation condition of the brake friction vibration model after parameter optimization according to the Hopf bifurcation theory in nonlinear dynamics, and calculating the amplitude of the unstable limit cycle generated by the subcritical Hopf bifurcation;

[0019] Step 32, setting parameters, and obtaining the amplitude of the stick-slip vibration limit cycle according to the numerical simulation of the brake friction vibration model;

[0020] Step 33, compare the amplitude of the unstable limit cycle generated by the subcritical Hopf bifurcation with the amplitude of the stick-slip vibration limit cycle. If the two are equal, the corresponding point is the critical point;

[0021] Step 34, changing the parameters, repeating the steps 31 to 33, obtaining critical points under different parameters, and constructing a critical point set;

[0022] Step 33, fitting the critical point set in step 34 to obtain the stick-slip vibration boundary of the brake.

[0023] In a second aspect, a device for actively avoiding stick-slip vibration of a brake based on braking torque distribution is provided, the active avoidance device comprising a memory and one or more processors, the memory storing executable code, and characterized in that when the processor executes the executable code, it is used to implement the method for actively avoiding stick-slip vibration of a brake based on braking torque distribution as described in any one of the first aspects.

[0024] The beneficial effects of the present invention are as follows: compared with traditional passive methods such as structural matching, friction material selection and surface treatment, the present invention is an active suppression method, which can achieve active avoidance of brake stick-slip vibration by adjusting the regenerative braking torque and friction braking torque of the hybrid braking system on the basis of obtaining the stick-slip vibration boundary, and has good adaptability to changes in the working environment; there is no need to transform the mechanical structure of the hybrid braking system, and only improvements are made in the algorithm, so there is no additional hardware cost; by regularly training and updating the neural network model, a better active suppression effect of stick-slip vibration can be obtained; the replacement of brake blocks or brake discs due to simple stick-slip vibration is avoided, the service life of parts is increased, and costs are saved. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0026] Figure 1 1 is a schematic diagram of the overall process of a brake stick-slip vibration active avoidance method based on braking torque distribution provided in one embodiment of the present invention;

[0027] Figure 2 is a Hopf bifurcation and an unstable limit cycle diagram thereof provided in one embodiment of the present invention;

[0028] Figure 3 is an intersection diagram of an unstable limit cycle and a stick-slip vibration limit cycle provided in one embodiment of the present invention;

[0029] Figure 4 are the first five groups of regenerative braking energy recovery efficiency diagrams provided in one embodiment of the present invention;

[0030] Figure 5 Schematic diagram of the structure of a brake stick-slip vibration active avoidance device based on braking torque distribution provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0032] like Figure 1 In one embodiment, a brake stick-slip vibration active avoidance method based on braking torque distribution is provided, and the active avoidance method comprises the following steps:

[0033] Step S1, constructing a brake friction vibration model based on a multi-body dynamics model described by a group of ordinary differential equations, wherein the multi-body dynamics model involves brake pad vibration and brake disc torsional vibration;

[0034] Step S2, sequentially identifying and optimizing the parameters of the brake friction vibration model through measured vehicle braking data and braking vibration data;

[0035] In some embodiments, the parameters of the brake friction vibration model are sequentially identified and optimized by measuring the vehicle braking data and the braking vibration data, including:

[0036] Step S21, carrying out a real vehicle test according to a typical cycle working condition, obtaining vehicle braking data and braking vibration data through a CAN bus and a vibration acquisition device, etc., and pre-processing the vehicle braking data and braking vibration data for identification of brake friction vibration model parameters;

[0037] Step S22, calculating the amplitude, fundamental frequency and energy of the vehicle based on the vehicle vibration data in step S21;

[0038] The vehicle braking data in step S21 is used as input data and input into the brake friction vibration model that has been parameter-identified to calculate the amplitude, fundamental frequency and energy of the braking vibration;

[0039] Step S23, subtracting the two sets of amplitude, fundamental frequency and energy in step S22 respectively, and constructing a multi-objective weighted function including amplitude, fundamental frequency and energy with unknown parameters of the brake friction vibration model as variables;

[0040] Step S24, based on the multi-objective weighted function in step S23, the optimal values ​​of the unknown parameters of the brake friction vibration model are obtained through a multi-objective particle swarm optimization algorithm.

[0041] In some embodiments, Among them, J r is the moment of inertia of the brake disc, c r is the brake disc damping coefficient, k r is the brake disc stiffness coefficient, θ r is the angular displacement of the brake disc, is the angular velocity of the brake disc, is the angular acceleration of the brake disc, m b is the mass of the brake pad, c b is the damping coefficient of the brake pad, x b is the stiffness coefficient of the brake pad, x b is the displacement of the brake pad, is the speed of the brake pad, is the acceleration of the brake pad, T b is the friction torque, F b For friction.

[0042]

[0043] in

[0044]

[0045] μ(v r ) is the friction coefficient, which changes with the relative speed v r Change; F N is the brake pressure; r b is the radius of the brake disc, v r is the relative speed between the brake pad and the brake disc, ω is the initial angular velocity of the brake disc; μ k is the dynamic friction coefficient, Δμ is the variation of the friction coefficient, Δμ=μ s -μ k , μ s is the static friction coefficient, α is the velocity attenuation coefficient describing the change of friction coefficient, sign(v r ) represents v r The direction of the , positive value is +1, negative value is -1.

[0046] Specifically, this embodiment adopts given parameters, and the parameters of the braking system are selected as follows: r =0.65kgm 2 , m b =2kfg, k r =3*10 5 Nm / rad,k b =3*10 5 Nm / rad,c r =1Nmrad / s,c b =1Nmrad / s, r b =0.12m,μ s =0.5, μ k =0.3,α=4.6.

[0047] Step S3, based on the brake friction vibration model after parameter optimization, obtain the brake stick-slip vibration boundary through nonlinear dynamics theory;

[0048] In some embodiments, based on the brake friction vibration model after parameter optimization, the brake stick-slip vibration boundary is obtained through nonlinear dynamics theory, including:

[0049] Step 31, setting parameters, deriving the subcritical Hopf bifurcation condition of the brake friction vibration model after parameter optimization according to the Hopf bifurcation theory in nonlinear dynamics, and calculating the amplitude of the unstable limit cycle generated by the subcritical Hopf bifurcation;

[0050] Step 32, setting parameters, and obtaining the amplitude of the stick-slip vibration limit cycle according to the numerical simulation of the brake friction vibration model;

[0051] Step 33, compare the amplitude of the unstable limit cycle generated by the subcritical Hopf bifurcation with the amplitude of the stick-slip vibration limit cycle. If the two are equal, the corresponding point is the critical point;

[0052] Step 34, changing the parameters, repeating steps 31 to 33, obtaining critical points under different parameters, and constructing a critical point set;

[0053] Step 33, fitting the critical point set in step 34 to obtain the stick-slip vibration boundary of the brake.

[0054] The derivation process of Hopf bifurcation in this embodiment is as follows: Let x 1 =θ r , x 3 =x b , Take sign(v r )=1, the mathematical model of the disc brake can be written as:

[0055]

[0056] T m is the additional torque of the braking system. Let equation (5) equal to zero, then its equilibrium point can be expressed as x 0 =[x 10 ,0,x 30 , 0], where:

[0057]

[0058] The Jacobian matrix of formula (5) at the equilibrium point is:

[0059]

[0060] in,

[0061] The characteristic equation of the Jacobian matrix can be expressed as:

[0062] λ4 +a 3 λ 3 +a 2 λ 2 +a 1 λ+a 0 =0 (8)

[0063] Among them, λ is the eigenvalue of the Jacobian matrix, which characterizes the stability and oscillation characteristics of the system;

[0064]

[0065] λ 1，2 = ±is Substituting into the characteristic equation (8), we can get:

[0066] s 4 -a 2 s 2 +a 0 =0 (9)

[0067] -a 3 s 2 +a 1 =0 (10) Where s is the complex frequency, representing the oscillation mode; According to equations (10) and (11), we can get:

[0068]

[0069] The transversality condition for Hopf bifurcation is:

[0070]

[0071] in, is the bifurcation parameter, which is used to describe the transition of the system from stable to unstable; is the corresponding bifurcation value. It is the real part of the derivative of the eigenvalue with respect to the bifurcation parameter, which is used to determine whether the system has Hopf bifurcation.

[0072] According to the Hopf bifurcation condition, the bifurcation characteristics of the equilibrium point can be obtained by numerical analysis as follows: Figure 2 As shown in the figure, "SubH" is the subcritical Hopf bifurcation point, and "E 0 " is the unstable equilibrium point, "E 1 " is the stable equilibrium point, and "LC" represents the amplitude of the unstable limit cycle. Figure 2 It can be seen that the unstable equilibrium point "E 0 "After passing the subcritical Hopf bifurcation point, it turns into a stable equilibrium point" E 1 ”, and an unstable limit cycle will be generated.

[0073] According to formula (5), construct the Poincaré section x4 = 0, the stick-slip vibration limit cycle can be obtained with the speed ω, and it can be combined with Figure 2 By superposition, we can get that the stick-slip vibration limit cycle and the unstable limit cycle intersect at ω = 12.23, see Figure 3 Repeat the above steps to get the intersection point set, and then use the polynomial fitting method to get ω and F N The relationship is shown in formula (13).

[0074] F N =0.000616ω 3 +0.06468ω 2 +1.1495ω-0.013819 (13)

[0075] Step S4, setting the stick-slip vibration boundary of the brake as a constraint condition, taking maximizing the regenerative braking energy recovery efficiency as the goal, optimizing the regenerative braking energy recovery efficiency based on typical cycle conditions, and using this as input data to train the neural network model;

[0076] The objective function of the regenerative braking efficiency η in this embodiment is:

[0077] Maxη(Z, SOC, k_V v) (14)

[0078] The constraints are:

[0079]

[0080] In the formula, F max is the maximum regenerative braking force; T max is the maximum torque of the motor; n is the motor speed; n o is the rated speed of the motor; R is the wheel radius; P max is the maximum power of the motor; P bmax is the maximum charging power; k V is the vehicle speed influencing factor; SOC is the battery's state of charge (State of Charge), which indicates the remaining battery power ratio.

[0081] Formula (13) is also set as one of the constraints. The regenerative braking energy recovery efficiency values ​​obtained by the fuzzy algorithm of particle swarm optimization are shown in Table 1.

[0082] Table 1 Energy recovery efficiency of the first five groups

[0083]

[0084] In this embodiment, the energy recovery efficiency obtained by three deep learning algorithms, namely, Optimizable Neural Network (ONN), Optimizable Ensemble Tree (OTEL) and Long Short-Term Memory Network (LSTM) is as follows: Figure 4shown.

[0085] In order to select a suitable algorithm, the goodness of fit (R 2 ) and root mean square error (RMSE) are used as evaluation indicators of the model, and their relationship is:

[0086]

[0087] In the formula, y t p is the true value of regeneration efficiency; y t BP，p is the predicted value of regeneration efficiency; is the average value of regeneration efficiency. 2 It is the Coefficient of Determination, which is an indicator to measure the prediction ability of the model. The closer it is to 1, the better. RMSE is the root mean square error, which is a standard to measure the prediction error.

[0088] From Table 2, we can see that OTEL’s R 2 and RMSE were 0.97, 0.92, 0.98, 0.95 and 0.017, 0.026, 0.014, 0.020, respectively, while the R 2 and RMSE are 0.82, 0.83, 0.85, 0.79 and 0.042, 0.038, 0.038, 0.043, respectively, while the R 2 and RMSE are 0.88, 0.89, 0.86, 0.88 and 0.034, 0.042, 0.037, 0.033, respectively. Overall, OTEL has the best prediction effect, with R 2 The OTEL model has a better prediction effect.

[0089] Table 2 Evaluation indicators of ONN, OTEL, and LSTM

[0090]

[0091]

[0092] Step S5, predicting the regenerative braking energy recovery efficiency through the trained neural network model, and then calculating the braking torque distribution coefficient of the hybrid braking system that can take into account the stick-slip vibration suppression of the brake, and distributing the braking torque according to the braking torque distribution coefficient.

[0093] Compared with traditional passive methods such as structural matching, friction material selection and surface treatment, the present invention is an active suppression method. On the basis of obtaining the stick-slip vibration boundary, by adjusting the regenerative braking torque and friction braking torque of the hybrid braking system, the stick-slip vibration of the brake can be actively avoided, and it has good adaptability to changes in the working environment; there is no need to modify the mechanical structure of the hybrid braking system, only to make improvements in the algorithm, so there is no additional hardware cost; by regularly training and updating the neural network model, a better active suppression effect of stick-slip vibration can be obtained; avoiding the replacement of brake blocks or brake discs due to simple stick-slip vibration, increasing the service life of parts and saving costs.

[0094] See also Figure 5 An embodiment of the present invention provides a device for actively avoiding stick-slip vibration of a brake based on braking torque distribution, comprising a memory and one or more processors. The memory stores executable code. When the processor executes the executable code, it is used to implement the method for actively avoiding stick-slip vibration of a brake based on braking torque distribution in the above embodiment.

[0095] The embodiments of the active avoidance method of brake stick-slip vibration based on braking torque distribution of the present invention can be applied to any device with data processing capability, and the device with data processing capability can be a device or apparatus such as a computer. The device embodiment can be implemented through software, or through hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capability in which it is located reading the corresponding computer program instructions in the non-volatile memory into the internal memory for execution. From the hardware level, if Figure 5 As shown, it is a hardware structure diagram of any device with data processing capability in which the brake stick-slip vibration active avoidance method based on braking torque distribution of the present invention is located. Figure 5 In addition to the processor, memory, network interface, and non-volatile memory shown, any device with data processing capabilities in which the apparatus in the embodiments is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0096] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0097] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, in which the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0098] An embodiment of the present invention further provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the method for actively avoiding stick-slip vibration of a brake based on braking torque distribution in the above embodiment is implemented.

[0099] The computer-readable storage medium may be an internal storage unit of any device with data processing capability in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device of any device with data processing capability, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capability. The computer-readable storage medium is used to store computer programs and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0100] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0101] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0102] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0103] The above description is merely a preferred embodiment of one or more embodiments of the present invention and is not intended to limit one or more embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present invention shall be included in the scope of protection of one or more embodiments of the present invention.

Claims

1. A brake stick-slip vibration active avoidance method based on braking torque distribution, characterized in that: The following steps are involved: Based on a multi-body dynamics model described by a set of ordinary differential equations, a brake friction vibration model is constructed, wherein the multi-body dynamics model involves brake pad vibration and brake disc torsional vibration; sequentially identifying and optimizing the parameters of the brake friction vibration model by measuring vehicle braking data and braking vibration data; Based on the brake friction vibration model after parameter optimization, the stick-slip vibration boundary of the brake is obtained through nonlinear dynamics theory. The stick-slip vibration boundary of the brake is set as a constraint condition, and the regenerative braking energy recovery efficiency is optimized based on typical cycle conditions with the goal of maximizing the regenerative braking energy recovery efficiency. This is used as input data to train the neural network model. The regenerative braking energy recovery efficiency is predicted through the trained neural network model, and then the braking torque distribution coefficient of the hybrid braking system that can take into account the stick-slip vibration suppression of the brake is calculated, and the braking torque is distributed according to the braking torque distribution coefficient.

2. The active avoidance method for brake stick-slip vibration based on braking torque distribution according to claim 1 is characterized in that: The parameters of the brake friction vibration model are sequentially identified and optimized by measuring the vehicle braking data and vibration data, including: Step S21, obtaining vehicle braking data and braking vibration data, and pre-processing the vehicle braking data and braking vibration data for identification of brake friction vibration model parameters; Step S22, calculating the amplitude, fundamental frequency and energy of the braking vibration based on the braking vibration data in step S21; The vehicle braking data in step S21 is used as input data and input into the brake friction vibration model that has been parameter-identified to calculate the amplitude, fundamental frequency and energy of the vehicle; Step S23, respectively subtracting the two sets of amplitude, fundamental frequency and energy in step S22, and constructing a multi-objective weighted function including amplitude, fundamental frequency and energy with unknown parameters of the brake friction vibration model as variables; Step S24, based on the multi-objective weighted function in step S23, the optimal values ​​of the unknown parameters of the brake friction vibration model are obtained through a multi-objective particle swarm optimization algorithm.

3. The active avoidance method of brake stick-slip vibration based on braking torque distribution according to claim 1 is characterized in that: Based on the brake friction vibration model with optimized parameters, the stick-slip vibration boundary of the brake is obtained through nonlinear dynamics theory, including: Step 31, setting parameters, deriving the subcritical Hopf bifurcation condition of the brake friction vibration model after parameter optimization according to the Hopf bifurcation theory in nonlinear dynamics, and calculating the amplitude of the unstable limit cycle generated by the subcritical Hopf bifurcation; Step 32, setting parameters, and obtaining the amplitude of the stick-slip vibration limit cycle according to the numerical simulation of the brake friction vibration model; Step 33, compare the amplitude of the unstable limit cycle generated by the subcritical Hopf bifurcation with the amplitude of the stick-slip vibration limit cycle. If the two are equal, the corresponding point is the critical point; Step 34, changing the parameters, repeating the steps 31 to 33, obtaining critical points under different parameters, and constructing a critical point set; Step 33, fitting the critical point set in step 34 to obtain the stick-slip vibration boundary of the brake.

4. A brake stick-slip vibration active avoidance device based on braking torque distribution, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the processor executes the executable code, it is used to implement the active avoidance method of brake stick-slip vibration based on braking torque distribution as described in any one of claims 1-3.

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