Braking control method and device for electric vehicle

Abstract

The present application provides a braking control method and device for an electric vehicle, the method comprising: constructing a dynamic model of a target vehicle; obtaining vehicle parameters and braking requirements; performing fuzzy processing calculations based on the dynamic model, vehicle parameters and braking requirements to obtain target brake fluid pressures of the front and rear brake lines; performing hydraulic pressure tracking control calculations based on the target brake fluid pressures to obtain an optimal braking torque; and performing braking control on the target vehicle based on the optimal braking torque. It can be seen that the method and device can combine fuzzy control and hydraulic pressure tracking control to achieve optimal control of the braking system, thereby being able to intelligently adjust the braking system, which is conducive to handling complex driving conditions and environmental changes, and improving the stability and controllability of the vehicle under complex working conditions.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a braking control method and device for an electric vehicle. Background Art

[0002] With the increasing attention paid to environmental protection and sustainable development around the world, electric vehicle technology is developing at an unprecedented speed. At the same time, the development of control systems has also provided a solid foundation for the intelligence and automation of electric vehicles. The existing brake control methods usually collect vehicle status information through sensors first; then dynamically adjust the vehicle control parameters based on the collected information; monitor the vehicle status in real time, and control the vehicle based on the monitoring results. However, in practice, it is found that the existing methods cannot change the brake torque distribution of the front and rear axles of the vehicle by controlling the brake fluid pressure, which limits the stability and controllability of the vehicle under complex working conditions. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a braking control method and device for an electric vehicle, which can combine fuzzy control and hydraulic pressure tracking control to achieve optimal control of the braking system, thereby being able to intelligently adjust the braking system, which is conducive to handling complex driving conditions and environmental changes, and improving the stability and controllability of the vehicle under complex working conditions.

[0004] The first aspect of the present application provides a braking control method for an electric vehicle, comprising:

[0005] Constructing a dynamic model of a target vehicle; wherein the dynamic model includes a five-degree-of-freedom vehicle dynamic model and an energy sustainability model;

[0006] Obtain vehicle parameters and braking requirements;

[0007] Perform fuzzy processing calculation according to the dynamic model, the vehicle parameters and the braking demand to obtain target brake fluid pressures of the front and rear brake lines;

[0008] Perform hydraulic pressure tracking control calculation according to the target brake hydraulic pressure to obtain an optimal brake torque;

[0009] The target vehicle is braked and controlled according to the optimal braking torque.

[0010] Wherein, constructing the dynamic model of the target vehicle includes:

[0011] Construct a five-degree-of-freedom vehicle dynamics model of the target vehicle based on the dynamic characteristics of the system;

[0012] Building energy sustainability models;

[0013] The energy sustainability model includes the traction efficiency of each tire on the road surface, the total regenerative power input to the battery, the sustainable efficiency of the electricity, the current rate of the regenerative electric energy input to the battery, and the rate of change of the battery charge in the motor control unit over time;

[0014] The vehicle resistance calculated according to the five-degree-of-freedom vehicle dynamics model is the input of the energy sustainability model, and the output charge of the energy sustainability model is the input electric energy of the motor control unit.

[0015] Based on the above implementation process, this method can combine fuzzy control and hydraulic pressure tracking control to achieve optimal control of the braking system, thereby being able to intelligently adjust the braking system, which is conducive to handling complex driving conditions and environmental changes, and improving the stability and controllability of the vehicle under complex working conditions.

[0016] Furthermore, the fuzzy processing calculation is performed according to the dynamic model, the vehicle parameters and the braking demand to obtain the target brake fluid pressure of the front and rear brake lines, including:

[0017] receiving a voltage input signal provided by the frequency converter according to the braking demand;

[0018] Calculating vehicle braking parameters according to the dynamic model and the vehicle parameters;

[0019] Correcting the voltage input signal according to the fuzzy controller and the vehicle braking parameters to obtain a rated charge;

[0020] Calculating a braking system design constant based on the rated charge and the vehicle braking parameters;

[0021] Calculating the mass vibration equations of the front and rear axles of the vehicle according to the braking system design constants and the vehicle braking parameters;

[0022] According to the electronic stability system improved by the fuzzy control strategy and the mass vibration equation, the relationship between the target brake fluid pressure and the brake torque is calculated;

[0023] The target brake fluid pressures of the front and rear brake lines are calculated according to the relationship between the target brake fluid pressure and the brake torque and the vehicle braking parameters.

[0024] Further, performing hydraulic pressure tracking control calculation according to the target brake hydraulic pressure to obtain the optimal brake torque includes:

[0025] Construct the error equation for closed-loop control of hydraulic pressure system;

[0026] Calculating the hydraulic pressure of the brake line according to the error equation and the target brake hydraulic pressure;

[0027] A tracking error function is constructed based on a nonlinear response algorithm of the optimal slip ratio;

[0028] Calculating a minimum tracking error based on the tracking error function and the hydraulic pressure of the brake line;

[0029] Updating the minimized tracking error according to a nonlinear prediction control model to obtain an updated minimized tracking error;

[0030] Optimal braking torques for the front and rear wheels are determined based on the updated minimized tracking error.

[0031] Further, the calculating the minimum tracking error according to the tracking error function and the hydraulic pressure of the brake line includes:

[0032] Determining the tracking error function as an objective function;

[0033] The objective function is dynamically optimized and calculated according to a particle swarm optimization algorithm and the hydraulic pressure of the brake line to obtain a minimized tracking error.

[0034] A second aspect of the present application provides a brake control device for an electric vehicle, the brake control device for the electric vehicle comprising:

[0035] A construction unit, used to construct a dynamic model of a target vehicle; wherein the dynamic model includes a five-degree-of-freedom vehicle dynamic model and an energy sustainability model;

[0036] An acquisition unit, used for acquiring vehicle parameters and braking requirements;

[0037] A first calculation unit, configured to perform fuzzy processing calculation according to the dynamic model, the vehicle parameters and the braking demand to obtain target brake fluid pressures of the front and rear brake lines;

[0038] A second calculation unit, configured to perform hydraulic pressure tracking control calculation according to the target brake hydraulic pressure to obtain an optimal brake torque;

[0039] a braking control unit, configured to perform braking control on the target vehicle according to the optimal braking torque;

[0040] Wherein, the construction unit is specifically used to construct a five-degree-of-freedom vehicle dynamics model of the target vehicle based on the dynamic characteristics of the system; and to construct an energy sustainability model; wherein, the energy sustainability model includes the traction efficiency of each tire on the road, the total regenerative power input to the battery, the sustainable efficiency of electricity, the current rate of regenerative electric energy input to the battery, and the rate of change of battery charge in the motor control unit over time; wherein, the vehicle resistance calculated according to the five-degree-of-freedom vehicle dynamics model is the input of the energy sustainability model, and the output charge of the energy sustainability model is the input electric energy of the motor control unit.

[0041] Furthermore, the first computing unit includes:

[0042] A receiving subunit, used for receiving a voltage input signal provided by the frequency converter according to the braking demand;

[0043] A first calculation subunit, used for calculating vehicle braking parameters according to the dynamic model and the vehicle parameters;

[0044] A correction subunit, used for correcting the voltage input signal according to the fuzzy controller and the vehicle braking parameter to obtain a rated charge;

[0045] a second calculation subunit, for calculating a brake system design constant according to the rated charge and the vehicle brake parameter;

[0046] The second calculation subunit is further used to calculate the mass vibration equation of the front and rear axles of the vehicle according to the braking system design constants and the vehicle braking parameters;

[0047] The second calculation subunit is further used to calculate the relationship between the target brake fluid pressure and the brake torque according to the electronic stability system improved by the fuzzy control strategy and the mass vibration equation;

[0048] The second calculation subunit is further used to calculate the target brake fluid pressure of the front and rear brake lines according to the relationship between the target brake fluid pressure and the braking torque and the vehicle braking parameters.

[0049] Furthermore, the second computing unit includes:

[0050] Constructing subunits for constructing error equations for closed-loop control of the hydraulic pressure system;

[0051] a third calculation subunit, configured to calculate the hydraulic pressure of the brake line according to the error equation and the target brake hydraulic pressure;

[0052] The construction subunit is further used to construct a tracking error function according to a nonlinear response algorithm of an optimal slip ratio;

[0053] The third calculation subunit is further used to calculate the minimum tracking error according to the tracking error function and the hydraulic pressure of the brake pipeline;

[0054] An updating subunit, used for updating the minimized tracking error according to a nonlinear prediction control model to obtain an updated minimized tracking error;

[0055] A determination subunit is used to determine the optimal braking torque of the front and rear wheels according to the updated minimized tracking error.

[0056] Furthermore, the third computing subunit includes:

[0057] A determination module, used to determine the tracking error function as an objective function;

[0058] The calculation module is used to perform dynamic optimization calculation on the objective function according to the particle swarm optimization algorithm and the hydraulic pressure of the brake pipeline to obtain a minimized tracking error.

[0059] A third aspect of the present application provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the braking control method for an electric vehicle described in any one of the first aspects of the present application.

[0060] A fourth aspect of the present application provides a computer-readable storage medium storing computer program instructions, wherein when the computer program instructions are read and executed by a processor, the braking control method of the electric vehicle described in any one of the first aspect of the present application is executed.

[0061] The beneficial effects of the present application are as follows: based on the five-degree-of-freedom vehicle dynamics model, energy consumption such as air resistance and rolling resistance is incorporated into the system dynamics equation, thereby improving the accuracy of analyzing and modeling the dynamic characteristics of the vehicle system; at the same time, the energy sustainability model converts its resistance energy consumption into kinetic energy through the motor, which can achieve sustainable power output and motion control of the vehicle system;

[0062] In addition, in the design of the braking system, the electronic stability program is adjusted through the fuzzy control strategy, which can more effectively control the brake fluid pressure output by the hydraulic module; at the same time, the nonlinear predictive control method can be used to determine the optimal control strategy for the braking force, thereby achieving comprehensive consideration of the rotational dynamics of the front and rear wheels; finally, the use of a hydraulic pressure tracking control algorithm in the hydraulic system can also achieve precise control of the hydraulic pressure in the hydraulic system, thereby improving the braking control accuracy of electric vehicles, and thereby ensuring the stability and controllability of electric vehicles under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A schematic flow chart of a braking control method for an electric vehicle provided in an embodiment of the present application;

[0065] Figure 2 A schematic diagram of a design flow of a braking control method for an electric vehicle provided in an embodiment of the present application;

[0066] Figure 3 A schematic flow chart of another braking control method for an electric vehicle provided in an embodiment of the present application;

[0067] Figure 4 A schematic diagram of an example flow chart of a braking control method for an electric vehicle provided in an embodiment of the present application;

[0068] Figure 5 A schematic diagram of the structure of a brake control device for an electric vehicle provided in an embodiment of the present application;

[0069] Figure 6 A schematic structural diagram of another brake control device for an electric vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0071] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0072] Example 1

[0073] Please see Figure 1 , Figure 1 The present invention provides a flow chart of a braking control method for an electric vehicle. The braking control method for an electric vehicle includes:

[0074] S101. Construct a dynamic model of a target vehicle.

[0075] In this embodiment, the method preferentially constructs a five-degree-of-freedom dynamics model and an energy sustainability model and uses them as the dynamics model of the target vehicle.

[0076] In this embodiment, the method first selects a five-degree-of-freedom vehicle dynamics model based on an electric vehicle to provide a model basis for energy transmission, and then determines the input of the energy sustainability model based on the air resistance and rolling resistance in the five-degree-of-freedom dynamics modeling.

[0077] It can be understood that the electrical energy is first transferred from the battery to the motor control unit, and then converted into kinetic energy through the motor, and the energy sustainable model outputs the kinetic energy to the motor control unit.

[0078] In this embodiment, the energy sustainability model includes the traction efficiency of each tire on the road, the total regenerative power input to the battery, the sustainable efficiency of electricity, the current rate of regenerative electrical energy input to the battery, and the rate of change of battery charge in the motor control unit over time.

[0079] In this embodiment, the vehicle resistance calculated according to the five-degree-of-freedom vehicle dynamics model is the input of the energy sustainability model, and the output charge of the energy sustainability model is the input electric energy of the motor control unit.

[0080] S102: Obtain vehicle parameters and braking requirements.

[0081] S103, performing fuzzy processing calculation according to the dynamic model, vehicle parameters and braking demand to obtain target brake fluid pressures of the front and rear brake lines.

[0082] In this embodiment, the method involves improved control of an electronic stability program (ESP) system, specifically adopting a fuzzy control strategy to improve the ESP system by changing the brake torque distribution of the front and rear axles of the vehicle. The fuzzy control method is based on fuzzy logic, fuzzy mathematics, and fuzzy language variable generation, and has intelligent characteristics.

[0083] In this embodiment, the method mainly uses a hydraulic module to control the brake fluid pressure, and the obtained brake fluid pressure is used as the output of this model.

[0084] S104: Perform hydraulic pressure tracking control calculation according to the target brake hydraulic pressure to obtain an optimal braking torque.

[0085] In this embodiment, the target brake fluid pressure includes the brake fluid pressure Q of the front brake line. j and the brake fluid pressure Q of the rear brake line k , this method can obtain Q based on the hydraulic pressure tracking control method j and Q k, this method can calculate the optimal braking torque of the front and rear wheels in stages. Among them, since the vehicle is equipped with a rear axle hub motor, the main mechanical braking system is responsible for generating appropriate braking torque on the front axle. At the same time, the mechanical and electrical braking systems generate braking torque on the rear axle.

[0086] In this embodiment, the braking strategy includes two braking torque control stages. First, the mechanical torque is controlled by the dynamics of the front wheels; second, the braking torque is controlled by the dynamics of the rear wheels. The control logic consists of the above two parts, maintaining the simplicity, modularity and performance safety of the control system.

[0087] S105: Perform braking control on the target vehicle according to the optimal braking torque.

[0088] Please see Figure 2 , Figure 2 A schematic diagram of the design process of a braking control method for an electric vehicle is shown.

[0089] In this embodiment, the execution subject of the method may be a computing device such as a computer or a server, and no limitation is made in this embodiment.

[0090] In this embodiment, the execution subject of the method may also be a smart device such as a smart phone, a tablet computer, etc., which is not limited in this embodiment.

[0091] It can be seen that the braking control method of the electric vehicle described in this embodiment can combine fuzzy control and hydraulic pressure tracking control to achieve optimal control of the braking system, thereby being able to intelligently adjust the braking system, which is conducive to handling complex driving conditions and environmental changes, and improving the stability and controllability of the vehicle under complex working conditions.

[0092] Example 2

[0093] Please see Figure 3 , Figure 3 The present invention provides a flow chart of a braking control method for an electric vehicle. The braking control method for an electric vehicle includes:

[0094] S201. Construct a five-degree-of-freedom vehicle dynamics model of the target vehicle based on the dynamic characteristics of the system.

[0095] In this embodiment, the method is to determine the energy consumption of the electric vehicle, and a five-degree-of-freedom model is used to model the dynamics of the vehicle, as shown below:

[0096] (1)

[0097] Among them, ν x is the vehicle lateral velocity, ν yis the longitudinal velocity of the vehicle, ω is the wheel angular velocity, m is the mass of the vehicle, F tj is the longitudinal force of the front wheel of the vehicle, F tk is the longitudinal force of the rear wheel of the vehicle, α is the angle between the front and rear axles of the vehicle, ω j is the front wheel speed, ω k is the rear wheel speed, W is the vehicle drag coefficient, ρ is the air density, S is the frontal area, I is the moment of inertia, F bj is the rolling resistance of the front wheels of the vehicle, F bk is the rolling resistance of the rear wheels of the vehicle, T bj is the braking torque of the front wheels of the vehicle, T bk is the braking torque of the rear wheels of the vehicle, d j is the longitudinal distance of the front axle of the vehicle, d k is the longitudinal distance of the vehicle's rear axle.

[0098] S202. Build an energy sustainability model.

[0099] In this embodiment, the energy sustainability model includes the traction efficiency of each tire on the road, the total regenerative power input to the battery, the sustainable efficiency of electricity, the current rate of regenerative electrical energy input to the battery, and the rate of change of battery charge in the motor control unit over time.

[0100] In this embodiment, the rolling resistance F of the tire in the five-degree-of-freedom dynamic model is bi and longitudinal force F tj 、F tk The equal resistance is used as the input of the energy sustainability model, and the charge output by the energy sustainability model is used as the input electric energy of the motor control unit. Among them, air resistance, rolling resistance and longitudinal force are collectively referred to as vehicle resistance.

[0101] Among them, this method also considers the power conversion and transmission efficiency of the tire-road contact surface when analyzing the overall efficiency of the vehicle. Specifically, this method models the energy sustainability of electric vehicles and designs the energy loss between the tire and the contact surface, including the traction efficiency R of each tire on the road. Ti And the total regenerative power P input to the battery b The definition is as follows:

[0102] (2)

[0103] (3)

[0104] Among them, P ti is the longitudinal motion power, P ci is the total mechanical efficiency, T i is the torque in the vehicle braking system, ω i is the angular velocity in the vehicle braking system, νx is the vehicle lateral velocity, ν y is the longitudinal velocity of the vehicle, F ti is the longitudinal force of the wheel. When i=j, F ti =F tj , when i=k, F ti =F tk , F tj is the longitudinal force of the front wheel of the vehicle, F tk is the longitudinal force of the rear wheel of the vehicle, ω j is the front wheel speed, ω k is the rear wheel speed, λ is the proportion of regenerative braking to total braking energy determined by the braking strategy, R b is the total efficiency of the energy sustainable process during braking, P bj is the braking power of the front wheels of the vehicle during braking, P bk It is the braking power of the rear wheels of the vehicle during braking.

[0105] In this embodiment, since the motor in the electric vehicle is located on the rear axle of the vehicle, the sustainable efficiency of the electric power R b Defined for the rear axle only:

[0106] (4)

[0107] Among them, R c is the efficiency related to battery cell charging, R e is the efficiency of the motor controller, R M is the efficiency of the motor, f a is the copper loss, T E is the torque of the motor, R T is the traction efficiency of the rear axle tire and the road surface, θ E is the angular velocity of the motor, f c is the winding loss, and el is the constant mechanical loss inside the motor.

[0108] In this embodiment, since the model car uses a hub motor on the rear axle, the angular velocity θ of the motor is E Equal to the rear axle wheel speed θ r The current rate of the regenerative power input to the battery is V e And the rate of change of battery charge in the motor control unit over time S(t+Δt) is as follows:

[0109] (5)

[0110] (6)

[0111] Among them, T E is the torque of the motor, θ E is the angular velocity of the motor, Rb is the total efficiency of the energy sustainable process during braking, B is the voltage level of the battery pack, Q c is the nominal capacity of the battery cell, Q e is the number of parallel cells in the battery pack, f a is the copper loss, ζ is the time step, U is the nominal voltage of the battery cell, Q s is the number of batteries connected in series in the battery pack, θ r is the rear axle wheel speed.

[0112] S203: Obtain vehicle parameters and braking requirements.

[0113] S204, receiving a voltage input signal provided by the inverter according to a braking requirement.

[0114] S205: Calculate vehicle braking parameters according to the dynamics model and vehicle parameters.

[0115] In this embodiment, the vehicle braking parameters may include the effective radius of the contact area between the brake pad and the brake disc, the effective area of ​​the brake piston in the braking system, the displacement of the vehicle body center of mass, negative acceleration, the height of the spring center of mass, the vehicle pitch angle, and the vertical displacement of the front and rear tires.

[0116] S206: Correct the voltage input signal according to the fuzzy controller and the vehicle braking parameters to obtain the rated charge.

[0117] In this embodiment, in order to capture the final brake fluid pressure, the method uses the voltage signal U provided by the frequency converter as the input of the fuzzy control, which is corrected by the fuzzy controller and acts on the ESP to complete the control process of the entire model.

[0118] Among them, the rated charge U obtained by the fuzzy controller e for:

[0119] (7)

[0120] (8)

[0121] (9)

[0122] Among them, T E is the torque of the motor, ω i is the angular velocity, X and Y are the process functions of the fuzzy controller, p and q are constants that can be preset, U is the nominal voltage of the battery cell, z i is the mechanical efficiency of the motor during braking, and the calculation symbol Indicates the exclusive or logical operation, r i It is the effective radius of the contact area between the brake pad and the brake disc.

[0123] S207: Calculate the braking system design constants according to the rated charge and the vehicle braking parameters.

[0124] In this embodiment, the fuzzy control strategy obtains the rated load U e After that, the braking system design constant η of the ratio of the braking torque caused by the front and rear wheel brake fluid pressure Q is calculated as follows:

[0125] (10)

[0126] Where I is the moment of inertia, ω i is the angular velocity, ν x is the vehicle lateral velocity, ν y is the longitudinal speed of the vehicle, d is the longitudinal distance between the front and rear axles of the vehicle, r i is the effective radius of the contact area between the brake pad and the brake disc, A j and A k It is the effective area of ​​the brake piston in the brake system.

[0127] S208. Calculate the mass vibration equations of the front and rear axles of the vehicle according to the braking system design constants and the vehicle braking parameters.

[0128] In this embodiment, based on the system design constants, the mass vibration equations of the front and rear axles of the vehicle can be obtained:

[0129] (11)

[0130] (12)

[0131] (13)

[0132] Among them, m 1 is the mass of the front axle, m 2 is the mass of the rear axle, x 1 is the vertical displacement of the front tire, x 2 is the vertical displacement of the rear tire, h c is the height of the spring center of mass, α is the vehicle pitch angle, η is the braking system design constant, and a b is negative acceleration, g 1 is the stiffness of the front suspension, g 2 is the stiffness of the rear suspension, x * is the displacement of the vehicle body center of mass, d j is the longitudinal distance of the front axle of the vehicle, d k is the longitudinal distance of the vehicle's rear axle, k 1 k is the response coefficient of the front axle when the vehicle pitch angle changes during braking, 2 is the response coefficient of the rear axle when the vehicle pitch angle changes during braking;

[0133] Among them, m 1 and m 2 is the actual value that has been measured and can be set in advance; g 1 and g 2 It is the actual value that has been measured and can be preset.

[0134] S209. Calculate the relationship between the target brake fluid pressure and the brake torque according to the electronic stability system and mass vibration equation improved by the fuzzy control strategy.

[0135] S210: Calculate the target brake fluid pressures of the front and rear brake lines according to the relationship between the target brake fluid pressure and the brake torque and the vehicle braking parameters.

[0136] In this embodiment, the ESP improved by the fuzzy control strategy obtains the target brake fluid pressure Q j and Q k With braking torque T i Relationship:

[0137] (14)

[0138] Among them, Q j For the appropriate brake fluid pressure in the front brake line, Q k is the appropriate brake fluid pressure for the rear brake line, m 1 is the mass of the front axle, m 2 is the mass of the rear axle, a b is negative acceleration, h c is the height of the spring center of mass, η is the braking system design constant, x 1 is the vertical displacement of the front tire, x 2 is the vertical displacement of the rear tire, α is the vehicle pitch angle, d j is the longitudinal distance of the front axle of the vehicle, d k is the longitudinal distance of the vehicle's rear axle.

[0139] Among them, T i is the braking torque, when i=j, T i =T j , when i=k, T i =T k , T j is the braking torque of the front wheels of the vehicle, T k is the braking torque of the vehicle's rear wheels.

[0140] S211. Construct the error equation for closed-loop control of the hydraulic pressure system.

[0141] In this embodiment, based on the target hydraulic pressure Q j and Q kThe method proposes a hydraulic pressure tracking control method, the goal is to make the actual hydraulic pressure Q and the target hydraulic pressure Q j and Q k The error equation u converges to zero to ensure that the actual hydraulic pressure can track the target hydraulic pressure and maintain the closed-loop stability of the hydraulic pressure system. The error equation of the closed-loop control model of the hydraulic pressure system is:

[0142]

[0143]

[0144]

[0145] (15)

[0146] Among them, k 0 and δ are both positive numbers and are preset parameters. is the hydraulic pressure tracking error, Q is the actual hydraulic pressure, Q j For the appropriate brake fluid pressure in the front brake line, Q k Proper brake fluid pressure for the rear brake lines.

[0147] S212: Calculate the hydraulic pressure of the brake line according to the error equation and the target brake hydraulic pressure.

[0148] S213: construct a tracking error function according to a nonlinear response algorithm of an optimal slip ratio.

[0149] S214: Determine the tracking error function as the target function.

[0150] S215, dynamically optimizing and calculating the objective function according to the particle swarm optimization algorithm and the hydraulic pressure of the brake line to obtain a minimized tracking error.

[0151] In this embodiment, the method can be based on the hydraulic pressure Q(t) of the brake line obtained by formula (15) and the nonlinear response σ of the optimal slip ratio. * (t+τ), and further obtain the minimized tracking error E as follows:

[0152] (16)

[0153] Where τ is the time interval.

[0154] In this embodiment, the method introduces a nonlinear prediction control model:

[0155] (17)

[0156] Among them, θ α and θ βis the adaptive gain, σ ^ is the optimal slip value, λ is the time constant, and e is the natural constant.

[0157] Therefore, the minimized tracking error E can be updated as:

[0158] (18)

[0159] Where, Q(t) is the hydraulic pressure in the brake line, τ is the time interval, θ α and θ β is the adaptive gain, σ ^ is the optimal slip value, and λ is the time constant.

[0160] In this embodiment, the method can be based on the above model to convert the torque T in the vehicle braking system into i and angular velocity ω i The parameters are taken as input, and the objective function is to minimize the tracking error of formula (18). The particle swarm optimization algorithm is used to dynamically optimize this objective function.

[0161] The dynamic optimization of the particle swarm optimization algorithm is explained as follows:

[0162] First, in the particle swarm optimization method, one group of particles updates its relative position from iteration to another group of particles, which promotes the particle swarm optimization algorithm to correctly execute the search process. In order to obtain the optimal solution, the objective function f to be optimized in formula (18) is used as the optimization target of the particle swarm optimization method, and the optimization process is defined as follows:

[0163]

[0164] in, is the optimal position of particle i in the tth iteration, is the global optimal position of the tth iteration, t is the number of current iterations, is the position of particle i in the current iteration, is the current position of the optimal particle, and the objective function is the tracking error equation of formula (18).

[0165] Secondly, each particle has its own velocity and location The calculation formula is as follows:

[0166]

[0167] in, represents the velocity vector, ω t is the inertia weight, r 1 、r 2is a random number uniformly distributed in the range [0, 1], c 1 、c 2 is the acceleration factor, is the optimal position of particle i in the tth iteration, is the global optimal position for the tth iteration.

[0168] Finally, the method optimizes the particle swarm algorithm as follows:

[0169] (1) Initialize the relevant parameters of the particle swarm optimization algorithm: random number r 1 、r 2 , inertia weight ω t , acceleration factor c 2 ;

[0170] (2) Assign the particles to the control input of the braking torque system in turn, obtain the current tracking error of the group of parameters, and calculate the fitness function of the group of parameters according to formula (18);

[0171] (3) Compare the current positions of all particles with the optimal value. If it is not optimal, continue to iterate and finally retain the optimal individual particle;

[0172] (4) Use the method in step (2) to continuously update the particle's velocity and position and calculate a new fitness function;

[0173] (5) Determine whether the loop condition meets the end condition. If the maximum number of iterations has not been reached, jump to step (3); otherwise, end the program and return a set of parameters corresponding to the optimal solution;

[0174] (6) The final set of parameters returned is calculated to obtain the minimum tracking error.

[0175] It can be seen that this method can use the particle swarm optimization algorithm to dynamically adjust and optimize the braking system, realize the minimization calculation of the tracking error problem of the braking system, thereby ensuring real-time control and optimization of the tracking error, and effectively improving the performance and stability of the braking system.

[0176] S216: updating the minimized tracking error according to the nonlinear prediction control model to obtain an updated minimized tracking error.

[0177] In this embodiment, a nonlinear predictive control method is adopted to comprehensively consider the rotational dynamics of the front and rear wheels, and the optimal control strategy for the braking force is determined in combination with the usage constraints of the electric regenerative torque. The distribution of the braking force is dynamically adjusted according to the real-time vehicle status and driving requirements to ensure that the vehicle can maintain its direction and speed more stably during braking.

[0178] S217: Determine the optimal braking torque of the front and rear wheels according to the updated minimized tracking error.

[0179] In this embodiment, regarding the rear axle braking torque, this method can use the tracking error obtained by formula (18) as the input of the rear axle braking system, and finally obtain the optimal braking torque :

[0180] (19)

[0181] Where E is the minimum tracking error, λ is the time constant, σ ^ is the optimal slip value, θ α and θ β is the adaptive gain, τ is the time interval, δ is the adaptation factor, θ 1 and θ 2 is a random number uniformly distributed in the range [0, 1], T D is the rear axle braking torque, T E is the torque of the motor.

[0182] It can be understood that when the slip ratio exceeds the optimal value, θ α and θ β are all equal to 1, It can be written as follows:

[0183] (20)

[0184] Among them, ν x is the lateral speed of the vehicle, τ is the time interval, I is the moment of inertia, η is the design constant of the braking system, P 0 is the initial hydraulic pressure in the brake system, θ 1 and θ 2 is a random number uniformly distributed in the range [0, 1], T D is the rear axle braking torque, T E is the torque of the motor.

[0185] S218: Perform braking control on the target vehicle according to the optimal braking torque.

[0186] In this embodiment, the method can achieve improved control of the vehicle braking system by controlling the hydraulic module to output the brake fluid pressure.

[0187] For example, the method may include:

[0188] (1) Design of five-degree-of-freedom dynamic model and energy sustainability model

[0189] The energy sustainability model and the five-degree-of-freedom dynamics model can jointly analyze and model the dynamic characteristics of the system, taking into account the impact of external environmental factors such as the vehicle's suspension system and tire slip on the system, introducing external factors such as air resistance and rolling resistance into the system dynamics equation, and outputting kinetic energy (kinetic energy converted from resistance by the motor) to the motor control unit to achieve the system's power output and energy sustainability control.

[0190] (2) Design of electronic stability system based on fuzzy control strategy improvement

[0191] The electronic stability program (ESP) system based on fuzzy control strategy can intelligently adjust the electronic stability program through fuzzy control strategy, thereby improving the vehicle's driving stability and safety; at the same time, it uses fuzzy logic and fuzzy mathematics to handle complex driving conditions and environmental changes.

[0192] (3) Design of closed-loop control model based on hydraulic pressure tracking control method

[0193] The method can reduce the error between the actual hydraulic pressure and the target hydraulic pressure through the hydraulic pressure tracking control method, adjust the parameters of the control algorithm in real time, ensure that the actual hydraulic pressure can track the target hydraulic pressure and maintain the closed-loop stability of the hydraulic pressure system. It can be seen that the method can adjust the control parameters in the hydraulic system so that the closed-loop control of the system can gradually adjust the actual hydraulic pressure to be consistent with the target hydraulic pressure, thereby ensuring the stable operation of the braking system.

[0194] (4) Optimal braking torque design

[0195] The method obtains the optimal braking torque in stages, formulates the braking torque control logic of the front and rear wheels, and obtains the optimal braking torque of the front and rear wheels according to vehicle parameters and braking requirements.

[0196] (5) Performance evaluation and optimization: The strategy and the designed control system are applied to the in-wheel motor electric vehicle, taking into account all battery and vehicle limitations and conditions, and fully meeting all standard requirements. The specifications of the simulated vehicle are set as vehicle mass m = 1340 kg, impact mass ms = 1756 kg, vehicle center of gravity height h = 0.44 m, vehicle wheelbase d = 2.347 m, wheel radius R = 0.34563 m, moment of inertia I = 1245 kgm 2 , air density ρ = 1.22 5kg / m 3 .

[0197] Please see Figure 4 , Figure 4 An example flow chart of a braking control method for an electric vehicle is shown.

[0198] In this embodiment, the execution subject of the method may be a computing device such as a computer or a server, and no limitation is made in this embodiment.

[0199] In this embodiment, the execution subject of the method may also be a smart device such as a smart phone, a tablet computer, etc., which is not limited in this embodiment.

[0200] It can be seen that the braking control method of the electric vehicle described in this embodiment can combine fuzzy control and hydraulic pressure tracking control to achieve optimal control of the braking system, thereby being able to intelligently adjust the braking system, which is conducive to handling complex driving conditions and environmental changes, and improving the stability and controllability of the vehicle under complex working conditions.

[0201] Example 3

[0202] Please see Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the structure of a brake control device for an electric vehicle provided in this embodiment. Figure 5 As shown, the brake control device of the electric vehicle includes:

[0203] A construction unit 310 is used to construct a dynamic model of the target vehicle; wherein the dynamic model includes a five-degree-of-freedom vehicle dynamic model and an energy sustainability model;

[0204] An acquisition unit 320, used to acquire vehicle parameters and braking requirements;

[0205] A first calculation unit 330 is used to perform fuzzy processing calculation according to the dynamic model, vehicle parameters and braking requirements to obtain target brake fluid pressures of the front and rear brake lines;

[0206] A second calculation unit 340 is used to perform hydraulic pressure tracking control calculation according to the target brake hydraulic pressure to obtain an optimal braking torque;

[0207] The brake control unit 350 is used to perform brake control on the target vehicle according to the optimal brake torque.

[0208] Among them, the construction unit 310 is specifically used to construct a five-degree-of-freedom vehicle dynamics model of the target vehicle based on the dynamic characteristics of the system; and to construct an energy sustainability model; wherein the energy sustainability model includes the traction efficiency of each tire on the road, the total regenerative power input to the battery, the sustainable efficiency of the electricity, the current rate of the regenerative electric energy input to the battery, and the rate of change of the battery charge in the motor control unit over time; wherein the vehicle resistance calculated according to the five-degree-of-freedom vehicle dynamics model is the input of the energy sustainability model, and the output charge of the energy sustainability model is the input electric energy of the motor control unit.

[0209] In this embodiment, the explanation of the brake control device of the electric vehicle can refer to the description in Embodiment 1 or Embodiment 2, and will not be further elaborated in this embodiment.

[0210] It can be seen that the braking control device of the electric vehicle described in this embodiment can combine fuzzy control and hydraulic pressure tracking control to achieve optimal control of the braking system, thereby being able to intelligently adjust the braking system, which is conducive to handling complex driving conditions and environmental changes, and improving the stability and controllability of the vehicle under complex working conditions.

[0211] Example 4

[0212] Please see Figure 6 , Figure 6 FIG. 1 is a schematic diagram of the structure of a brake control device for an electric vehicle provided in this embodiment. Figure 6 As shown, the brake control device of the electric vehicle includes:

[0213] A construction unit 310 is used to construct a dynamic model of the target vehicle; wherein the dynamic model includes a five-degree-of-freedom vehicle dynamic model and an energy sustainability model;

[0214] An acquisition unit 320, used to acquire vehicle parameters and braking requirements;

[0215] A first calculation unit 330 is used to perform fuzzy processing calculation according to the dynamic model, vehicle parameters and braking requirements to obtain target brake fluid pressures of the front and rear brake lines;

[0216] A second calculation unit 340 is used to perform hydraulic pressure tracking control calculation according to the target brake hydraulic pressure to obtain an optimal braking torque;

[0217] The brake control unit 350 is used to perform brake control on the target vehicle according to the optimal brake torque.

[0218] As an optional implementation, the construction unit 310 is specifically used to construct a five-degree-of-freedom vehicle dynamics model of the target vehicle based on the dynamic characteristics of the system; and to construct an energy sustainability model; wherein the energy sustainability model includes the traction efficiency of each tire on the road, the total regenerative power input to the battery, the sustainable efficiency of the electricity, the current rate of the regenerative electric energy input to the battery, and the rate of change of the battery charge in the motor control unit over time; wherein the vehicle resistance calculated according to the five-degree-of-freedom vehicle dynamics model is the input of the energy sustainability model, and the output charge of the energy sustainability model is the input electric energy of the motor control unit.

[0219] As an optional implementation, the first calculation unit 330 includes:

[0220] A receiving subunit 331 is used to receive a voltage input signal provided by the frequency converter according to a braking demand;

[0221] A first calculation subunit 332, used for calculating vehicle braking parameters according to the dynamics model and vehicle parameters;

[0222] The correction subunit 333 is used to correct the voltage input signal according to the fuzzy controller and the vehicle braking parameters to obtain the rated charge;

[0223] A second calculation subunit 334 is used to calculate a brake system design constant according to the rated charge and the vehicle brake parameters;

[0224] The second calculation subunit 334 is further used to calculate the mass vibration equation of the front and rear axles of the vehicle according to the braking system design constants and the vehicle braking parameters;

[0225] The second calculation subunit 334 is further used to calculate the relationship between the target brake fluid pressure and the brake torque according to the electronic stability system and the mass vibration equation improved by the fuzzy control strategy;

[0226] The second calculation subunit 334 is further used to calculate the target brake fluid pressure of the front and rear brake lines according to the relationship between the target brake fluid pressure and the braking torque and the vehicle braking parameters.

[0227] As an optional implementation, the second calculation unit 340 includes:

[0228] A construction subunit 341 is used to construct an error equation for closed-loop control of the hydraulic pressure system;

[0229] A third calculation subunit 342 is used to calculate the hydraulic pressure of the brake line according to the error equation and the target brake hydraulic pressure;

[0230] The construction subunit 341 is further used to construct a tracking error function according to a nonlinear response algorithm of an optimal slip ratio;

[0231] The third calculation subunit 342 is further used to calculate the minimum tracking error according to the tracking error function and the hydraulic pressure of the brake line;

[0232] An updating subunit 343, used for updating the minimized tracking error according to the nonlinear prediction control model to obtain an updated minimized tracking error;

[0233] The determination subunit 344 is used to determine the optimal braking torque of the front and rear wheels according to the updated minimized tracking error.

[0234] As an optional implementation, the third calculation subunit 342 includes:

[0235] A determination module, used for determining the tracking error function as the target function;

[0236] The calculation module is used to perform dynamic optimization calculation on the objective function according to the particle swarm optimization algorithm and the hydraulic pressure of the brake pipeline to obtain the minimized tracking error.

[0237] In this embodiment, the explanation of the brake control device of the electric vehicle can refer to the description in Embodiment 1 or Embodiment 2, and will not be further elaborated in this embodiment.

[0238] It can be seen that the braking control device of the electric vehicle described in this embodiment can combine fuzzy control and hydraulic pressure tracking control to achieve optimal control of the braking system, thereby being able to intelligently adjust the braking system, which is conducive to handling complex driving conditions and environmental changes, and improving the stability and controllability of the vehicle under complex working conditions.

[0239] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the braking control method of the electric vehicle in Embodiment 1 or Embodiment 2 of the present application.

[0240] An embodiment of the present application provides a computer-readable storage medium storing computer program instructions. When the computer program instructions are read and executed by a processor, the braking control method of the electric vehicle in Embodiment 1 or Embodiment 2 of the present application is executed.

[0241] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0242] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0243] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0244] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0245] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0246] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A braking control method for an electric vehicle, characterized in that: include: Constructing a dynamic model of a target vehicle; wherein the dynamic model includes a five-degree-of-freedom vehicle dynamic model and an energy sustainability model; Obtain vehicle parameters and braking requirements; Perform fuzzy processing calculation according to the dynamic model, the vehicle parameters and the braking demand to obtain target brake fluid pressures of the front and rear brake lines; Perform hydraulic pressure tracking control calculation according to the target brake hydraulic pressure to obtain an optimal brake torque; performing braking control on the target vehicle according to the optimal braking torque; Wherein, constructing the dynamic model of the target vehicle includes: Construct a five-degree-of-freedom vehicle dynamics model of the target vehicle based on the dynamic characteristics of the system; Building energy sustainability models; The energy sustainability model includes the traction efficiency of each tire on the road surface, the total regenerative power input to the battery, the sustainable efficiency of the electricity, the current rate of the regenerative electric energy input to the battery, and the rate of change of the battery charge in the motor control unit over time; The vehicle resistance calculated according to the five-degree-of-freedom vehicle dynamics model is the input of the energy sustainability model, and the output charge of the energy sustainability model is the input electric energy of the motor control unit; Wherein, the five-degree-of-freedom vehicle dynamics model is: Among them, ν x is the vehicle lateral velocity, ν y is the longitudinal velocity of the vehicle, ω is the wheel angular velocity, m is the mass of the vehicle, F tj is the longitudinal force of the front wheel of the vehicle, F tk is the longitudinal force of the rear wheel of the vehicle, α is the angle between the front and rear axles of the vehicle, ω j is the front wheel speed, ω k is the rear wheel speed, W is the vehicle drag coefficient, ρ is the air density, S is the frontal area, I is the moment of inertia, F bj is the rolling resistance of the front wheels of the vehicle, F bk is the rolling resistance of the rear wheels of the vehicle, T bj is the braking torque of the front wheels of the vehicle, T bk is the braking torque of the rear wheels of the vehicle, d j is the longitudinal distance of the front axle of the vehicle, d k is the longitudinal distance of the vehicle's rear axle.

2. The braking control method of an electric vehicle according to claim 1, characterized in that: The fuzzy processing calculation is performed according to the dynamic model, the vehicle parameters and the braking demand to obtain the target brake fluid pressure of the front and rear brake lines, including: receiving a voltage input signal provided by the frequency converter according to the braking demand; Calculating vehicle braking parameters according to the dynamic model and the vehicle parameters; Correcting the voltage input signal according to the fuzzy controller and the vehicle braking parameters to obtain a rated charge; Calculating a braking system design constant based on the rated charge and the vehicle braking parameters; Calculating the mass vibration equations of the front and rear axles of the vehicle according to the braking system design constants and the vehicle braking parameters; According to the electronic stability system improved by the fuzzy control strategy and the mass vibration equation, the relationship between the target brake fluid pressure and the brake torque is calculated; Calculating the target brake fluid pressures of the front and rear brake lines according to the relationship between the target brake fluid pressure and the brake torque and the vehicle braking parameters; Among them, the mass vibration equation is: Where m1 is the mass of the front axle, m2 is the mass of the rear axle, x1 is the vertical displacement of the front tire, x2 is the vertical displacement of the rear tire, and h c is the height of the spring center of mass, α is the vehicle pitch angle, η is the braking system design constant, and a b is the negative acceleration, g1 is the stiffness of the front suspension, g2 is the stiffness of the rear suspension, x * is the displacement of the vehicle body center of mass, d j is the longitudinal distance of the front axle of the vehicle, d k is the longitudinal distance of the rear axle of the vehicle, k1 is the response coefficient of the front axle when the vehicle pitch angle changes during braking, and k2 is the response coefficient of the rear axle when the vehicle pitch angle changes during braking.

3. The braking control method of an electric vehicle according to claim 1, characterized in that: The step of performing hydraulic pressure tracking control calculation according to the target brake hydraulic pressure to obtain an optimal brake torque includes: Construct the error equation for closed-loop control of hydraulic pressure system; Calculating the hydraulic pressure of the brake line according to the error equation and the target brake hydraulic pressure; A tracking error function is constructed based on a nonlinear response algorithm of the optimal slip ratio; Calculating a minimum tracking error based on the tracking error function and the hydraulic pressure of the brake line; Updating the minimized tracking error according to a nonlinear prediction control model to obtain an updated minimized tracking error; Optimal braking torques for the front and rear wheels are determined based on the updated minimized tracking error.

4. The braking control method of an electric vehicle according to claim 3, characterized in that: The step of calculating the minimum tracking error according to the tracking error function and the hydraulic pressure of the brake line comprises: Determining the tracking error function as an objective function; The objective function is dynamically optimized and calculated according to a particle swarm optimization algorithm and the hydraulic pressure of the brake line to obtain a minimized tracking error.

5. A brake control device for an electric vehicle, characterized in that: The brake control device of the electric vehicle comprises: A construction unit, used to construct a dynamic model of a target vehicle; wherein the dynamic model includes a five-degree-of-freedom vehicle dynamic model and an energy sustainability model; An acquisition unit, used for acquiring vehicle parameters and braking requirements; A first calculation unit, configured to perform fuzzy processing calculation according to the dynamic model, the vehicle parameters and the braking demand to obtain target brake fluid pressures of the front and rear brake lines; A second calculation unit, configured to perform hydraulic pressure tracking control calculation according to the target brake hydraulic pressure to obtain an optimal brake torque; a braking control unit, configured to perform braking control on the target vehicle according to the optimal braking torque; The construction unit is specifically used to construct a five-degree-of-freedom vehicle dynamics model of the target vehicle based on the dynamic characteristics of the system; and to construct an energy sustainability model; The energy sustainability model includes the traction efficiency of each tire on the road surface, the total regenerative power input to the battery, the sustainable efficiency of the electricity, the current rate of the regenerative electric energy input to the battery, and the rate of change of the battery charge in the motor control unit over time; The vehicle resistance calculated according to the five-degree-of-freedom vehicle dynamics model is the input of the energy sustainability model, and the output charge of the energy sustainability model is the input electric energy of the motor control unit; Wherein, the five-degree-of-freedom vehicle dynamics model is: Among them, ν x is the vehicle lateral velocity, ν y is the longitudinal velocity of the vehicle, ω is the wheel angular velocity, m is the mass of the vehicle, F tj is the longitudinal force of the front wheel of the vehicle, F tk is the longitudinal force of the rear wheel of the vehicle, α is the angle between the front and rear axles of the vehicle, ω j is the front wheel speed, ω k is the rear wheel speed, W is the vehicle drag coefficient, ρ is the air density, S is the frontal area, I is the moment of inertia, F bj is the rolling resistance of the front wheels of the vehicle, F bk is the rolling resistance of the rear wheels of the vehicle, T bj is the braking torque of the front wheels of the vehicle, T bk is the braking torque of the rear wheels of the vehicle, d j is the longitudinal distance of the front axle of the vehicle, d k is the longitudinal distance of the vehicle's rear axle.

6. The brake control device for an electric vehicle according to claim 5, characterized in that: The first computing unit comprises: A receiving subunit, used for receiving a voltage input signal provided by the frequency converter according to the braking demand; A first calculation subunit, used for calculating vehicle braking parameters according to the dynamic model and the vehicle parameters; A correction subunit, used for correcting the voltage input signal according to the fuzzy controller and the vehicle braking parameter to obtain a rated charge; a second calculation subunit, for calculating a brake system design constant according to the rated charge and the vehicle brake parameter; The second calculation subunit is further used to calculate the mass vibration equation of the front and rear axles of the vehicle according to the braking system design constants and the vehicle braking parameters; The second calculation subunit is further used to calculate the relationship between the target brake fluid pressure and the brake torque according to the electronic stability system improved by the fuzzy control strategy and the mass vibration equation; The second calculation subunit is further used to calculate the target brake fluid pressure of the front and rear brake lines according to the relationship between the target brake fluid pressure and the brake torque and the vehicle braking parameter; Among them, the mass vibration equation is: Where m1 is the mass of the front axle, m2 is the mass of the rear axle, x1 is the vertical displacement of the front tire, x2 is the vertical displacement of the rear tire, and h c is the height of the spring center of mass, α is the vehicle pitch angle, η is the braking system design constant, and a b is the negative acceleration, g1 is the stiffness of the front suspension, g2 is the stiffness of the rear suspension, x * is the displacement of the vehicle body center of mass, d j is the longitudinal distance of the front axle of the vehicle, d k is the longitudinal distance of the rear axle of the vehicle, k1 is the response coefficient of the front axle when the vehicle pitch angle changes during braking, and k2 is the response coefficient of the rear axle when the vehicle pitch angle changes during braking.

7. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the braking control method for an electric vehicle according to any one of claims 1 to 4.

8. A readable storage medium, characterized in that: The readable storage medium stores computer program instructions, and when the computer program instructions are read and executed by a processor, the braking control method of the electric vehicle according to any one of claims 1 to 4 is executed.

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