Analysis and Pre-stability Control Method and System for Random Instability of Electric Vehicle Braking

Abstract

This invention belongs to the field of automotive technology, specifically relating to a method and system for analyzing and pre-stabilizing the random instability of electric vehicle braking. The vehicle's drive motor adjusts the driving torque output to the wheels according to drive torque control parameters. The control method includes random instability analysis and pre-stabilization control. This invention differs from traditional solutions that only identify, respond to, and compensate for braking vibration after it has already occurred. Instead, it performs random instability analysis and pre-stabilization control during braking: it determines whether to trigger random instability analysis and pre-stabilization control based on wheel angular velocity. Random instability analysis uses stochastic dynamics theory to solve for the stability boundary of braking vibration, which is conservatively designed by introducing calibration constants. Then, pre-stabilization control obtains drive torque control parameters from the conservative stability boundary, thereby achieving early control before braking vibration occurs.

Description

Technical Field

[0001] This invention pertains to a control method and control system in the field of automotive technology, an automobile using the control system, and a braking control method using the control method. Specifically, it relates to an analytical and pre-stabilization control method for random instability during braking of an electric vehicle, an analytical and pre-stabilization control system for random instability during braking of an electric vehicle, an electric vehicle using the control system, and a braking control method for an electric vehicle using the control method. Background Technology

[0002] In recent years, distributed electric drive vehicles have seen significant improvements in their dynamic performance due to the technological advantage of independently controllable four-wheel drive torque. The braking system of distributed electric drive vehicles is a crucial component ensuring vehicle safety. Disc brakes have become widely used in recent years due to their high braking efficiency, reliability, and ease of maintenance. However, disc brakes are primarily based on friction braking, making them highly susceptible to brake shudder at low speeds. This not only increases braking distance and reduces driving safety but also negatively impacts ride comfort. Since brake shudder is induced by numerous factors, elucidating its mechanism and implementing active control remains a challenge in the field of vehicle dynamics research.

[0003] Existing technologies primarily rely on vibration signals after brake shudder occurs in automobiles to predict or identify brake shudder, and then use deterministic theory to obtain the stability boundary of the braking system for feedback or compensation control. For example, ZL202011615043.2, "A Brake Shudder Suppression Method and Device Based on Operating Conditions," monitors shudder occurring during braking using sensors and makes joint decisions by the primary and secondary control units to maintain the braking pressure at an optimal value, thereby suppressing brake shudder. Another example is ZL201110407289.5, "A Braking Shake Reduction Control System and Method for Four-Wheel Wheel-End Drive Electric Vehicles," which identifies braking torque fluctuation signals through braking pressure and pedal displacement signals, and controls the motor output to reduce the fluctuating motor torque, thereby reducing vehicle vibration caused by brake shudder and improving ride comfort. For example, ZL202211340883.1, "An Electric Wheel Braking Flutter Prediction and Control System and its Method", predicts flutter by obtaining the region of the steady-state boundary where the desired braking force signal and wheel angular velocity signal are located, and calculates the wheel cylinder braking pressure and the wheel hub motor compensation torque, thereby controlling the action of the braking wheel cylinder and the wheel hub motor to suppress flutter.

[0004] Therefore, it can be seen that when existing technologies are used to predict or identify vehicle brake shudder, the vehicle brake shudder phenomenon has already occurred, which has a negative impact on vehicle driving safety and comfort. Moreover, the uncertainty of system parameters leads to the randomness of the stability boundary. Summary of the Invention

[0005] To address the problem that existing technologies only identify and compensate for existing vehicle brake flutter phenomena, thus failing to effectively prevent the occurrence of such phenomena, this invention provides a method for analyzing and pre-stabilizing random instability of electric vehicle braking, a control system for analyzing and pre-stabilizing random instability of electric vehicle braking, an electric vehicle using the control system, and a braking control method for an electric vehicle using the control method.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0007] A method for analyzing and pre-stabilizing the random instability during braking of an electric vehicle includes the following steps:

[0008] Random instability analysis: When determining the wheel angular velocity at the initial braking moment of a car oh Less than or equal to the rated wheel angular velocity oh c At that time, pre-stabilization control is initiated;

[0009] Pre-stabilization control: based on oh Braking pressure at the initial stage of vehicle braking F N By optimizing the model, the proportional parameters for drive torque control are obtained. k p and differential parameters k d The stochastic stability boundary is defined by the regions inside and outside the boundary being the stable parameter region and the unstable parameter region, respectively. A point is randomly selected on the boundary or within the stable parameter region. k p and k d As an optimized k p and k d The car's drive motor is based on the optimized k p and k d Regulate the driving torque output to the wheels;

[0010] The optimized model design is as follows:

[0011]

[0012] In the formula, k r , I r , c rThese are the torsional stiffness, moment of inertia, and torsional damping of the brake disc. r for k r Random control parameters, D It is Gaussian white noise. 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. m k and m s These are the coefficients of sliding friction and static friction between the brake disc and brake pads, respectively. m b , c b These are the mass and damping of the brake pads, respectively. R It is the equivalent radius of action of the brake pad on the brake disc, and const is the calibration constant.

[0013] As a further improvement to the above scheme, with k p x-axis k d Establish for the ordinate k p k d The coordinate system in which the curve about the optimization model is plotted is the stochastic stability boundary.

[0014] As a further improvement to the above scheme, select k d =-1.2, then solve according to the stochastic stability boundary. k p .

[0015] As a further improvement to the above scheme, pre-stabilization control involves first... oh and F N Filtering is performed to remove noise signals.

[0016] As a further improvement to the above scheme, the design method of the optimization model includes the following steps:

[0017] Establish a two-degree-of-freedom dynamic model considering the brake disc rotation angle and brake block displacement:

[0018] (1)

[0019] In the formula, x、 , These are the displacement, velocity, and acceleration of the brake block, respectively. i , , These are the brake disc's rotation angle, angular velocity, and acceleration. x ( t ) is 2D Gaussian white noise. T r It is the braking flutter control torque. , F μ It is the braking friction force acting on the brake disc; k b It refers to the stiffness of the brake pads;

[0020] According to equation (1), we get F μ Simplified form:

[0021] (2)

[0022] In the formula, d 0、 d 1. d 2. d 3 are all intermediate variables: , , , Δ m It is an intermediate variable: Δ m = m s - m k , k 2 represents the coefficient of the cubic term in the friction model fitting of the automotive braking system;

[0023] Let the state variable , The Hamiltonian function of the automotive braking system is set as Equation (1) can be transformed into:

[0024] (3)

[0025] In the formula, m 11 , m 12 , m 21 , m 22 , s 11 , d 1. d 2. k 11 , k 12 , k 13 , k14 , k 21 , k 22 , k 23 These are all intermediate variables: , , , , ; , , , , , , , , , B ( t This is a standard Weiner process;

[0026] Equation (3) can be transformed into a one-dimensional average Iton stochastic differential equation, namely:

[0027] (4)

[0028] In the formula, m ( H ) is the drift coefficient. It is the diffusion coefficient, and its specific expression is:

[0029] (5)

[0030] In the formula, Q 1. Q 2. Q 3 are all intermediate variables: , , , It is about H Second order infinitesimal;

[0031] The Fokker Planck Kolmogorov equation derived from equation (5) is as follows:

[0032] (6)

[0033] In the formula, P st It is the steady probability density of the automotive braking system;

[0034] Integrating equation (6) yields the stationary probability density of the vehicle braking system as follows:

[0035] (7)

[0036] In the formula, C It is a normalized coefficient, and satisfies ;

[0037] Substituting equation (5) into equation (7) and ignoring higher-order terms, we get:

[0038] (8)

[0039] In the formula, , ;

[0040] Pick For the random stability index of automotive braking systems;

[0041] Solving by integrating equation (8), we get the answer regarding... q 2 and p The marginal probability density of 2 is:

[0042]

[0043] When the random stability index or When -1 < -1, the marginal probability density function is of a "single-peak" type, and the equilibrium solution of the automobile braking system is absolutely stable. or When the value is less than 0, the equilibrium solution of the vehicle braking system is probabilistically stable; to ensure the stability of the vehicle braking system, a conservative design is adopted for the stability boundary, namely... or ≤-1-const; therefore, take or =-1-const, thus obtaining the optimized model.

[0044] The present invention also provides an analytical and pre-stabilization control system for random instability during braking of an electric vehicle, comprising:

[0045] Used to collect the wheel angular velocity at the initial moment of vehicle braking. oh Wheel angular velocity sensor;

[0046] Used to collect braking pressure at the initial moment of vehicle braking. F N Brake pressure sensor;

[0047] Controller, used to determine oh and F N The above-mentioned random instability analysis and pre-stability control method for arbitrary electric vehicle braking is used to perform random instability analysis and pre-stability control for electric vehicle braking.

[0048] The present invention also provides an electric vehicle, comprising:

[0049] wheel;

[0050] Brake discs mounted on wheels;

[0051] The drive motor drives the wheels via the drive brake disc, and also controls the proportional parameters based on the drive torque. k p and differential parameters k d Regulate the driving torque output to the wheels;

[0052] Brake blocks used to brake the brake disc;

[0053] Used to collect the wheel angular velocity at the initial moment of vehicle braking. oh Wheel angular velocity sensor;

[0054] Used to collect braking pressure at the initial moment of vehicle braking. F N Brake pressure sensor;

[0055] The drive torque control parameter optimization unit is used to optimize the parameters according to the drive torque control parameters. oh and F N optimization k p and k d To regulate the driving torque: the above-mentioned analytical and pre-stabilization control method for random instability during braking of any electric-driven vehicle is used for optimization. k p and k d .

[0056] As a further improvement to the above solution, a wheel angular velocity sensor is mounted on the wheel.

[0057] As a further improvement to the above solution, the brake pressure sensor is mounted on the brake disc.

[0058] The present invention also provides a braking control method for an electric vehicle, which, when controlling the vehicle braking, specifies the wheel angular velocity at the initial stage of braking. oh Less than or equal to the rated wheel angular velocity oh c When the above-mentioned random instability analysis and pre-stabilization control method for the braking of any electric vehicle is activated, the braking flutter phenomenon of the electric vehicle is prevented.

[0059] Compared with the prior art, the intended effects of the present invention are as follows:

[0060] (1) Unlike traditional technical solutions that identify, respond to and compensate for vehicle braking shudder only after it has already occurred, this invention performs random instability analysis and pre-stabilization control during vehicle braking: it determines whether random instability analysis and pre-stabilization control are triggered based on wheel angular velocity. Random instability analysis is performed by solving the vehicle braking shudder stability boundary through stochastic dynamics theory. This boundary is conservatively designed by introducing calibration constants. Then, pre-stabilization control obtains the driving torque control parameters from the conservative stability boundary. Based on this, the vehicle driving torque is adjusted to achieve early control before the vehicle braking shudder occurs. Thus, this invention solves the problem of feedback control of braking shudder, which is mainly focused on in existing technical solutions, in order to enhance the braking stability and driving comfort of vehicles, especially distributed electric drive vehicles.

[0061] (2) To ensure the stability of the vehicle braking system, a conservative design is adopted for the stability boundary: the right side of the equation of the optimization model is designed as -1-const, where const>0. Therefore, the stochastic stability boundary formed by this concept ensures that even if unstable control parameters are selected near the stability boundary (i.e., within the unstable parameter region), the stability boundary is still stable. k p and k d This also ensures the stability control of vehicle braking chatter. Therefore, at any point on the random stability boundary or in the stable parameter region, the corresponding... k p and k d As an optimized k p and k d By adjusting the driving torque, it is certain that the stability control of vehicle braking vibration can be guaranteed without any doubt. Attached Figure Description

[0062] 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.

[0063] Figure 1 This is a schematic diagram of the module structure of the electric drive vehicle provided by the present invention.

[0064] Figure 2 For application Figure 1 The flowchart shows the analysis and pre-stabilization control method for random instability during braking of electric vehicles.

[0065] Figure 3 To adopt Figure 2 A schematic diagram of the stability boundary results obtained by the control method.

[0066] Figure 4 To adopt Figure 2 After the control method, with k p Taking 67050 N•m / rad as an example, when k d A schematic diagram of the random response at -0.60 N•m•s / rad.

[0067] Figure 5 To adopt Figure 2 After the control method, with k p Taking 67050 N•m / rad as an example, when k d A schematic diagram of the random response at -1.20 N•m•s / rad.

[0068] Figure 6 To adopt Figure 2 After the control method, with k p Taking 67050 N•m / rad as an example, when k d A schematic diagram of the random response at -1.40 N•m•s / rad. Detailed Implementation

[0069] 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.

[0070] Please see Figure 1 , Figure 1 This is a schematic diagram of the modular structure of an electric drive vehicle (such as a distributed electric drive vehicle) provided by the present invention. The electric drive vehicle includes wheels 1, flanges 2, brake discs 3, drive motors 4, a braking control system, and a braking random instability analysis and pre-stabilization control system. The brake discs 3 are mounted on the wheels 1 via the flanges 2. The drive motors 4 drive the wheels 1 to rotate by driving the brake discs 3, specifically by adjusting the proportional parameters in the drive torque control. k p and differential parameters k d The driving torque output to wheel 1 is adjusted to drive and control wheel 1. The braking control system is used to brake the operation of wheel 1. The braking random instability analysis and pre-stabilization control system is triggered under certain conditions when the braking control system is running to adjust the driving torque of drive motor 4, thereby achieving pre-stabilization control of vehicle braking shudder phenomenon and enhancing vehicle braking stability and ride comfort.

[0071] The braking control system of an electric vehicle may include brake pads 5, float calipers 6, pistons 7, and brake motors 8. Brake pads 5 are used to brake wheels 1, float calipers 6 are used to drive the braking operation of brake pads 5, and brake motors 8 drive float calipers 6 by driving the movement of pistons 7, thereby controlling brake pads 5 through float calipers 6 to achieve braking control of the movement of wheels 1.

[0072] The analysis and pre-stabilization control system for random instability during braking of an electric vehicle includes a wheel angular velocity sensor 9, a brake pressure sensor 10, and a controller 11. The wheel angular velocity sensor 9 is used to collect the wheel angular velocity at the initial stage of braking. oh Brake pressure sensor 10 is used to collect the brake pressure at the initial stage of vehicle braking. F N Controller 11 is used for analyzing and pre-stabilizing the random instability of electric vehicle braking.

[0073] The wheel angular velocity sensor 9 can be mounted on the brake disc 3. Since the brake disc 3 drives the wheel 1 to rotate, the wheel angular velocity sensor 9 can be collected by mounting it on the brake disc 3. The brake pressure sensor 10 can be mounted on the brake block 5. The brake pressure sensor 10 can also be mounted on the piston 7. The driving force of the piston 7 is proportional to the pressure on the brake block 5. Therefore, the braking pressure of the brake block 5 can be indirectly obtained by collecting the driving force of the piston 7.

[0074] Controller 11 is also called the drive torque control parameter optimization unit. Because controller 11 is used for analytical and pre-stabilization control of random instability during braking of electric vehicles, the control method of controller 11 is also the analytical and pre-stabilization control method for random instability during braking of electric vehicles. Please refer to... Figure 2 , Figure 2 The flowchart shows the analysis and pre-stabilization control method for random instability during braking of an electric vehicle, which includes two main steps: 1. Random instability analysis; 2. Pre-stabilization control.

[0075] Random instability analysis: When judging oh Less than or equal to the rated wheel angular velocity oh c If the condition is met, pre-stabilization control is activated; otherwise, it is not required. This applies to general passenger vehicles. oh c A value of 20 rad / s can be used. This is for the data acquisition... oh and F N Noise signals can be removed using filters. Since vehicle braking vibration mainly occurs under low-speed conditions, therefore... oh ≤ oh c When this occurs, the pre-stabilization control described in this invention can be triggered to achieve early intervention in vehicle braking vibration.

[0076] Pre-stabilization control: based on oh and F N By using a well-designed optimization model, we can obtain information about k p and k d The stochastic stability boundary is defined by the regions inside and outside the boundary being the stable parameter region and the unstable parameter region, respectively. A point is randomly selected on the boundary or within the stable parameter region. k p and k d As an optimized k p and k d To regulate the driving torque.

[0077] pass k p and k d Based on the determined stochastic stability boundary and the characteristics of vehicle braking vibration, the first step is to select... k d =-1.2, then solve according to the stochastic stability boundary. k p This optimizes the stability of control parameters, thereby enhancing the stability of the distributed electric drive vehicle braking system and preventing brake shudder. This can be determined first. k d Then use the stability boundary to solve for another one. k p .Well enough k p x-axis k d Establish for the ordinate k p k d A coordinate system is used to plot the curve about the optimization model, which represents the stochastic stability boundary. Then, a point corresponding to the stable parameter region is selected. k p and k d As an optimized k p and k d .

[0078] The optimized model design is as follows:

[0079]

[0080] Simplified to:

[0081]

[0082] In the formula, k r , I r , c r These are the torsional stiffness, moment of inertia, and torsional damping of the brake disc. r for k r Random control parameters, D It is Gaussian white noise. k 1 is the fitting coefficient. a It is the attenuation coefficient, Δ m It is an intermediate variable: Δ m = m s - m k , m k and m s These are the coefficients of sliding friction and static friction between the brake disc and brake pads, respectively. m b , c b These are the mass and damping of the brake pads, respectively. R It is the equivalent radius of action of the brake block on the brake disc, and const is a calibration constant used for conservative design of the stability boundary.

[0083] If we take the intermediate variable Δ m Substituting into the simplified formula, the final result is:

[0084]

[0085] This optimization model is the key point of this invention and is also the first of its kind. In order to better introduce the design principle of the optimization model, the design concept of the optimization model will be described in detail below.

[0086] I. Modeling of vehicle braking chatter considering uncertain stiffness.

[0087] Collect wheel angular velocity signals at the initial stage of vehicle braking oh and braking pressure signal F N A two-degree-of-freedom dynamic model considering the brake disc rotation angle and brake block displacement is established:

[0088] (1)

[0089] In the formula, x、 , These are the displacement, velocity, and acceleration of the brake block, respectively. i , , These are the brake disc's rotation angle, angular velocity, and acceleration, respectively. I r It is the moment of inertia of the brake disc; c r It is the torsional damping of the brake disc; k r It is the torsional stiffness of the brake disc; r It is the torsional stiffness of the brake disc. k r Random control parameters; x ( t ) is a strength of 2 D Gaussian white noise; R It is the equivalent radius of action of the brake pads on the brake disc; T r It is the braking flutter control torque. , k p and k d These are the brake flutter control parameters, which are the proportional and derivative parameters in the drive torque control. m b It is the mass of the brake pad; c b It is the damping of the brake pad; k b It refers to the stiffness of the brake pads; F μ This is the braking friction force acting on the brake disc, and its cubic expression can be simplified to:

[0090] (2)

[0091] In the formula, d 0、 d 1. d 2. d 3 are all intermediate variables: , , , 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 sIt is the coefficient of static friction between the brake disc and the brake pads. a It is the attenuation coefficient; k 1 and k 2 represents the fitting coefficients of the first and third terms of the friction model fitting of the automotive braking system.

[0092] Let the state variable , The Hamiltonian function of the automotive braking system is set as Equation (1) can be transformed into:

[0093] (3)

[0094] 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: , , , , ; , , , , , , , , , B ( t () is a standard Weiner process.

[0095] II. Solving for the stochastic stability index.

[0096] Equation (3) can be transformed into a one-dimensional average Iton stochastic differential equation, namely:

[0097] (4)

[0098] In the formula, m ( H ) is the drift coefficient. It is the diffusion coefficient, and its specific expression is:

[0099] (5)

[0100] In the formula, Q 1. Q 2. Q 3 are all intermediate variables: , , , It is about H Second order infinitesimal.

[0101] To analyze the stochastic stability of the automotive braking flutter system, the Fokker Planck-Kolmogorov equation derived from equation (5) is as follows:

[0102] (6)

[0103] In the formula, P st It is the steady probability density of the car braking system.

[0104] Integrating equation (6) yields the stationary probability density of the vehicle braking system as follows:

[0105] (7)

[0106] In the formula, C It is a normalized coefficient, and satisfies .

[0107] Substituting equation (5) into equation (7) and ignoring higher-order terms, we get:

[0108] (8)

[0109] In the formula, , .

[0110] According to stochastic dynamics theory, the coefficients or The value of this value can be used to determine the stability of a car's braking system; therefore, the stochastic stability index of a car's braking system is defined as follows: .

[0111] To analyze the dynamic response of the vehicle braking system, we integrate equation (8) and solve for the following: q 2 and p The marginal probability density of 2 is:

[0112] (9)

[0113] III. Optimization of Drive Torque Control Parameters

[0114] When the random stability index or When -1 < -1, the marginal probability density function is of a "single-peak" type, and the equilibrium solution of the automobile braking system is absolutely stable. or When the value is less than 0, the equilibrium solution of the vehicle braking system is probabilistically stable, indicating that the vehicle braking system converges to the equilibrium point with a high probability, but there is also a certain probability of vehicle brake chatter. Therefore, to ensure the stability of the vehicle braking system, a conservative design is adopted for the stability boundary, i.e. or ≤ or c =-1-const, where const>0, needs to be calibrated.

[0115] For the collected wheel angular velocity signals oh and braking pressure signal F N Noise signals can be removed by filtering. Since vehicle braking vibration mainly occurs under low-speed conditions, therefore... oh ≤ oh c When this occurs, the pre-stabilization control described in this invention is triggered, enabling early intervention in vehicle braking vibration. This is applicable to general passenger vehicles. oh c A value of 20 rad / s can be used. When the vehicle brake shudder pre-stabilization control method is triggered, it is based on the wheel angular velocity signal. oh and braking pressure signal F N ,pass or = or c Computing systems about k p and k d By determining the stochastic stability boundary, the stability of the control parameters can be optimized, thereby enhancing the stability of the distributed electric drive vehicle braking system and preventing the occurrence of vehicle brake shudder.

[0116] Therefore, the braking control method used in the electric drive vehicle of the present invention will control the vehicle braking, and at the initial stage of vehicle braking, the wheel angular velocity... oh Less than or equal to the rated wheel angular velocity oh cWhen the electric vehicle braking random instability analysis and pre-stabilization control method is activated, it prevents braking shudder from occurring. Therefore, in other embodiments, the electric vehicle braking control system may include an electric vehicle braking random instability analysis and pre-stabilization control system. This system is part of the electric vehicle braking control system. When the electric vehicle braking control system controls the vehicle braking, and when the wheel angular velocity at the initial stage of braking... oh Less than or equal to the rated wheel angular velocity oh c When the electric vehicle braking random instability analysis and pre-stabilization control system is activated, it prevents brake shudder from occurring. This system can only be activated when the electric vehicle braking control system is running.

[0117] To better verify the technical effects of this invention, experimental data is provided for verification and analysis. The parameters of the embodiment 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, c b =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, const=0.5, when the collected wheel angular velocity signal oh =10rad / s, brake block pressure is F N =1000N, the drive torque control parameters can be obtained. k p and k d Stability boundary such as Figure 3As shown, it is a schematic diagram of the stability boundary results in an embodiment of the present invention.

[0118] by k p Taking a value of 67050 N•m / rad as an example, different control parameters k d The random response is as follows Figure 4 , Figure 5 , Figure 6 As shown. When k d When = -0.60 N•m•s / rad, by Figure 3 It is known that the equilibrium solution of the automotive braking vibration system is probabilistically stable, and the system response mainly converges to the system equilibrium point, but if... Figure 4 The automotive braking system response shown also exhibits a stationary probability limit cycle, which degrades the vehicle's dynamic performance; when k d When = -1.20 N•m•s / rad, from Figure 3 It can be seen that although the control parameters are in the unstable parameter region at this time, due to the conservative design of the stability boundary, such as Figure 5 The vehicle braking system response shown also converges completely to the system equilibrium point, ensuring vehicle braking stability; when k d When the coefficient of friction is -1.40 N•m•s / rad, the equilibrium solution of the vehicle braking vibration system is absolutely stable, such as... Figure 6 The system response shown in the figure converges completely to the system equilibrium point. Clearly, the distributed electric drive vehicle braking flutter pre-stabilization control method proposed in this invention is effective.

[0119] The core advantage of this invention is:

[0120] 1) A two-degree-of-freedom stochastic dynamic model of vehicle braking flutter was established. The stochastic power spectral density function of the vehicle braking system was solved by stochastic dynamics theory, and the stochastic stability index of the vehicle braking system was defined.

[0121] 2) Based on the stochastic stability index, the pre-stabilization control trigger condition for vehicle brake flutter is set, thereby actively regulating the driving torque of the distributed electric drive vehicle and realizing the pre-stabilization control of vehicle brake flutter phenomenon, solving the problem that the existing technical solutions mainly focus on the feedback control of brake flutter.

[0122] 3) To ensure the stability of the vehicle braking system, a conservative design is adopted for the stability boundary: the right side of the equation in the optimization model is designed as -1 - const, where const > 0. Therefore, the stochastic stability boundary formed by this concept ensures that even if unstable control parameters are selected near the stability boundary (i.e., within the unstable parameter region), the stability boundary remains stable. k p andk d This also ensures the stability control of vehicle braking chatter. Therefore, at any point on the random stability boundary or in the stable parameter region, the corresponding... k p and k d As an optimized k p and k d By adjusting the driving torque, it is certain that the stability control of vehicle braking vibration can be guaranteed without any doubt.

[0123] In summary, the electric vehicle braking random instability analysis and pre-stabilization control system of the present invention can realize the functional upgrade of old vehicles by adding a brake flutter pre-stabilization control function to the braking control function. In application, wheel angular velocity sensors can be installed on the brake discs of old vehicles to collect the wheel angular velocity at the initial stage of vehicle braking. oh A brake pressure sensor is installed on the brake pads or pistons to collect the brake pressure at the initial stage of vehicle braking. F N Finally, a controller that is linked to the old car's drive motor can be installed in the car to upgrade the old car's functions. Of course, it is also possible to not install a controller, but design the controller's functions as software, thereby patching the old car's onboard system and using the updated onboard system to complete the controller's functions.

[0124] Alternatively, for new vehicles designed with the functions of this invention, wheel angular velocity sensors and brake pressure sensors are already installed at the factory. The vehicle's onboard system itself includes a drive torque control parameter optimization unit that can perform controller functions, eliminating the need for consumers to purchase an electric vehicle braking random instability analysis and pre-stability control system separately.

[0125] Therefore, the technology of this invention can be well applied to both new and old cars, making it easy to promote and apply, and possessing high technological transformation value and commercial prospects.

[0126] 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 analyzing and pre-stabilizing the random instability during braking of an electric vehicle, characterized in that, It includes the following steps: Random instability analysis: When determining the wheel angular velocity at the initial braking moment of a car ω Less than or equal to the rated wheel angular velocity ω c At that time, pre-stabilization control is initiated; Pre-stabilization control: based on ω Braking pressure at the initial stage of vehicle braking F N By optimizing the model, the proportional parameters for drive torque control are obtained. k p and differential parameters k d The stochastic stability boundary is defined by the regions inside and outside the boundary being the stable parameter region and the unstable parameter region, respectively. A point is randomly selected on the boundary or within the stable parameter region. k p and k d As an optimized k p and k d The car's drive motor is based on the optimized k p and k d Regulate the driving torque output to the wheels; The optimized model design is as follows: ； In the formula, k r , I r , c r These are the torsional stiffness, moment of inertia, and torsional damping of the brake disc. ρ for k r Random control parameters, D It is Gaussian white noise. 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. μ k and μ s These are the coefficients of sliding friction and static friction between the brake disc and brake pads, respectively. m b , c b These are the mass and damping of the brake pads, respectively. R It is the equivalent radius of action of the brake block on the brake disc; const is a calibration constant greater than zero.

2. The method for analyzing and pre-stabilizing the random instability of electric vehicle braking as described in claim 1, characterized in that, by k p x-axis k d Establish for the ordinate k p k d The coordinate system in which the curve about the optimization model is plotted is the stochastic stability boundary.

3. The method for analyzing and pre-stabilizing the random instability of electric vehicle braking as described in claim 1, characterized in that, Select k d =-1.2, solved according to the optimization model k p , respectively, as the optimized values ​​obtained at the boundary. k p and k d .

4. The method for analyzing and pre-stabilizing the random instability of electric vehicle braking as described in claim 1, characterized in that, Pre-stabilization control: During pre-stabilization control, first... ω and F N Filtering is performed to remove noise signals.

5. The method for analyzing and pre-stabilizing the random instability of electric vehicle braking as described in claim 1, characterized in that, The design method for optimizing the model includes the following steps: Establish a two-degree-of-freedom dynamic model considering the brake disc rotation angle and brake block displacement: （1） In the formula, x、 , These are the displacement, velocity, and acceleration of the brake block, respectively. θ , , These are the brake disc's rotation angle, angular velocity, and acceleration. ξ ( t ) is 2D Gaussian white noise. T r It is the braking flutter control torque. , F μ It is the braking friction force acting on the brake disc; k b It refers to the stiffness of the brake pads; According to equation (1), we get F μ Simplified form: （2） Where, d 0、 d 1. d 2. d 3 are all intermediate variables: , , , Δ μ It is an intermediate variable: Δ μ = μ s - μ k , κ 2 represents the coefficient of the cubic term in the friction model fitting of the automotive braking system; Let the state variable , The Hamiltonian function of the automotive braking system is set as Equation (1) can be transformed into: （3） In the formula, 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; Equation (3) can be transformed into a one-dimensional average Iton stochastic differential equation, namely: （4） In the formula, m ( H ) is the drift coefficient. It is the diffusion coefficient, and its specific expression is: （5） In the formula, Q 1. Q 2. Q 3 are all intermediate variables: , , , It is about H Second order infinitesimal; The Fokker Planck Kolmogorov equation derived from equation (5) is as follows: （6） Where, P st It is the steady probability density of the automotive braking system; Integrating equation (6) yields the stationary probability density of the vehicle braking system as follows: （7） Where, C It is a normalized coefficient and satisfies ; Substituting equation (5) into equation (7) and ignoring higher-order terms, we get: （8） In the formula, , ; Pick For the random stability index of automotive braking systems; Solving by integrating equation (8), we get the answer regarding... q 2 and p The marginal probability density of 2 is: ； When the random stability index η When -1 < -1, the marginal probability density function is of a "single-peak" type, and the equilibrium solution of the car braking system is absolutely stable. η When the value is less than 0, the equilibrium solution of the vehicle braking system is probabilistically stable; to ensure the stability of the vehicle braking system, a conservative design is adopted for the stability boundary, namely... η ≤-1-const; therefore, take η =-1-const, thus obtaining the optimized model.

6. A random instability analysis and pre-stability control system for electric vehicle braking, characterized in that, It includes: Used to collect the wheel angular velocity at the initial moment of vehicle braking. ω Wheel angular velocity sensor; Used to collect braking pressure at the initial moment of vehicle braking. F N Brake pressure sensor; Controller, based on ω and F N The method for analyzing and pre-stabilizing the random instability of electric vehicle braking as described in any one of claims 1 to 5 is used to perform analysis and pre-stabilization control of the random instability of electric vehicle braking.

7. An electric vehicle comprising: wheel; Brake discs mounted on wheels; The drive motor drives the wheels via the drive brake disc, and also controls the proportional parameters based on the drive torque. k p and differential parameters k d Regulate the driving torque output to the wheels; Brake blocks used to brake the brake disc; The electric vehicle is characterized in that it further includes: Used to collect the wheel angular velocity at the initial moment of vehicle braking. ω Wheel angular velocity sensor; Used to collect braking pressure at the initial moment of vehicle braking. F N Brake pressure sensor; The drive torque control parameter optimization unit is used to optimize the parameters according to the drive torque control parameters. ω and F N optimization k p and k d To regulate the driving torque: the method described in any one of claims 1 to 5 for analyzing and pre-stabilizing random instability during braking of electric vehicles is optimized. k p and k d .

8. The electric vehicle as described in claim 7, characterized in that, The wheel angular velocity sensor is mounted on the wheel.

9. The electric vehicle as described in claim 7, characterized in that, The brake pressure sensor is mounted on the brake disc.

10. A braking control method for an electric vehicle, characterized in that, It controls the wheel angular velocity when the car is braking, and at the initial moment of braking. ω Less than or equal to the rated wheel angular velocity ω c When the electric vehicle braking random instability analysis and pre-stabilization control method as described in any one of claims 1 to 5 is activated, the braking chatter phenomenon of the electric vehicle is prevented.

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