Hydraulic pressure control method for wheel cylinder of brake-by-wire system of electric vehicle

Abstract

The invention provides a hydraulic pressure control method for a wheel cylinder of a brake-by-wire system of an electric vehicle. The hydraulic pressure control method comprises the following steps: constructing a nonlinear model of an electronic hydraulic brake system of the electric vehicle; designing a self-adaptive expansion state observer based on an improved particle swarm optimization algorithm to estimate the initial hydraulic pressure of the wheel cylinder of the electric vehicle; taking the estimated initial hydraulic pressure of the wheel cylinder of the electric vehicle as input, and designing a sliding mode attitude controller based on a self-adaptive expansion state observer; the improved sliding mode control method is obtained by improving an objective function and a fitness function established by an alpha evolutionary algorithm, improving a sliding mode surface equation through the objective function and the fitness function, and introducing a chaotic disturbance term, an evolutionary compensation term and a self-adaptive buffeting suppression factor. And the hydraulic pressure of the wheel cylinder of the electric vehicle is controlled through an improved sliding mode control method. According to the invention, high-precision hydraulic pressure control can be realized, and the anti-interference capability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a method for controlling wheel cylinder hydraulic pressure of a wire-controlled brake system of an electric vehicle. Background Art

[0002] With the rapid development of intelligent and new energy technologies in the automotive industry, the reliability and safety of automotive braking systems, as a core component of vehicle safety, are becoming increasingly important. A vehicle's braking performance is directly related to driving and parking safety, as well as economic efficiency. In actual operation, electronic hydraulic braking systems may encounter various external interferences, such as sudden changes in road adhesion, load disturbances caused by driving on slopes, hydraulic oil viscosity shifts caused by ambient temperature fluctuations, hydraulic pressure instability, and nonlinear hysteresis. These interferences can affect the performance, stability, and safety of hydraulic braking systems.

[0003] Currently, most research on automotive electronic hydraulic brake system algorithms focuses on single control algorithms, such as PID control algorithms, MPC model predictive control algorithms, H∞ robust control algorithms, and fuzzy control algorithms. However, these algorithms can only achieve low-precision hydraulic pressure control to a certain extent and have weak anti-interference capabilities. Summary of the Invention

[0004] In view of this, the present invention provides a wheel cylinder hydraulic pressure control method for a wire-controlled brake system of an electric vehicle, so as to achieve high-precision hydraulic pressure control and enhance anti-interference capability.

[0005] A method for controlling wheel cylinder hydraulic pressure of a wire-controlled brake system of an electric vehicle, comprising: Step S1, constructing a nonlinear model of the electric vehicle electronic hydraulic brake system, and establishing a brake system mathematical model, a brake system dynamics balance equation, and a brake system dynamics model based on the constructed nonlinear model; Step S2, based on the brake system mathematical model, the brake system dynamics balance equation, and the brake system dynamics model established in step S1, designing an adaptive extended state observer based on an improved particle swarm optimization algorithm to estimate the preliminary electric vehicle wheel cylinder hydraulic pressure, wherein the improved particle swarm optimization algorithm is based on the particle swarm optimization algorithm and introduces Chebyshev chaos mapping, random inertia coefficient, and a gravitational search algorithm combined with Gaussian perturbation for optimization; Step S3, based on the adaptive extended state observer based on the improved particle swarm optimization algorithm designed in step S2, and using the estimated preliminary electric vehicle wheel cylinder hydraulic pressure as input, designing a sliding mode attitude controller based on the adaptive extended state observer; Step S4, based on the sliding mode attitude controller based on the adaptive extended state observer designed in step S3, by improving the objective function and fitness function established by the alpha evolution algorithm, improving the sliding surface equation through the objective function and fitness function, and introducing a chaotic disturbance term, an evolutionary compensation term, and an adaptive vibration suppression factor, thereby obtaining an improved sliding mode control method, and controlling the hydraulic pressure of the wheel cylinder of the electric vehicle through the improved sliding mode control method.

[0006] The method for controlling the hydraulic pressure of a wheel cylinder of a brake-by-wire system of an electric vehicle provided by the present invention has the following beneficial effects: (1) The present invention designs an adaptive extended state observer based on an improved particle swarm optimization algorithm, and optimizes the particle swarm optimization algorithm by combining Chebyshev chaos mapping, random inertia coefficient, gravitational search algorithm and Gaussian perturbation, which can improve the global search capability of the group.

[0007] (2) Based on the traditional hydraulic pressure control algorithm, the present invention improves the objective function and fitness function established by the Alpha evolution algorithm, improves the sliding surface equation through the objective function and fitness function, optimizes the dynamic response performance of the system, and enables the electric vehicle hydraulic brake system to remain stable when encountering external interference, significantly improving the vibration suppression capability; (3) In the sliding mode control method, the present invention introduces a chaotic disturbance term to enhance the global search capability to avoid local optimality. At the same time, the introduction of an evolutionary compensation term further improves the anti-interference ability. The adaptive vibration suppression factor can effectively weaken the vibration phenomenon of the sliding mode control, ultimately achieving high-precision and strong robustness wheel cylinder hydraulic pressure control. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flow chart of a method for controlling wheel cylinder hydraulic pressure in a brake-by-wire system of an electric vehicle provided by an embodiment of the present invention; Figure 2 This is a comparison diagram of the wheel cylinder hydraulic pressure obtained by the method of the present invention, the hydraulic pressure obtained by the traditional sliding mode control algorithm, and the ideal hydraulic pressure. DETAILED DESCRIPTION

[0009] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the embodiments of the present invention, and should not be construed as limiting the present invention.

[0010] See also Figure 1 An embodiment of the present invention provides a method for controlling wheel cylinder hydraulic pressure of a wire-controlled brake system of an electric vehicle, comprising steps S1-S4: Step S1, constructing a nonlinear model of the electric vehicle electronic hydraulic brake system, and establishing a brake system mathematical model, a brake system dynamics balance equation, and a brake system dynamics model based on the constructed nonlinear model.

[0011] In step S1, the mathematical model of the braking system is expressed as:

[0012]

[0013]

[0014] in, is the equivalent moment of inertia of the rotating part, is the angular acceleration, is the rated torque of the motor, is the torque on the first-stage ball screw, is the axial thrust of the ball screw, is the ball screw lead, is the transmission efficiency, is the gear ratio.

[0015] The expression of the dynamic equilibrium equation of the braking system is:

[0016] in, is the wheel cylinder mass, is the equivalent damping, is the equivalent spring stiffness of the wheel cylinder, is the piston displacement, for The first derivative of for The second derivative of is the piston area, is the wheel cylinder pressure.

[0017] Specifically, the relationship between the liquid pressure in the cavity is expressed as follows:

[0018]

[0019] in, is the wheel cylinder pressure change rate, is the brake fluid bulk modulus, is the volume change of the brake chamber liquid, is the initial liquid volume of the brake chamber, Output brake fluid flow to the brake wheel cylinder, is the brake fluid leakage coefficient of the brake wheel cylinder cavity.

[0020] The expression of the braking system dynamic model is:

[0021]

[0022] in, is the pressure change rate, is the brake chamber pressure.

[0023] Step S2, based on the brake system mathematical model, brake system dynamics balance equation and brake system dynamics model established in step S1, design an adaptive extended state observer based on the improved particle swarm optimization algorithm to estimate the preliminary electric vehicle wheel cylinder hydraulic pressure. The improved particle swarm optimization algorithm is based on the particle swarm optimization algorithm and introduces Chebyshev chaos mapping, random inertia coefficient, gravitational search algorithm and Gaussian perturbation to combine optimization.

[0024] First, the electric vehicle electronic hydraulic brake system is simplified into a second-order system and expressed as:

[0025] in, is the input quantity, Output The second derivative of is the total disturbance including all external disturbance factors, To control the gain.

[0026] Define intermediate state variables 、 、 , , , , Output The first-order derivative of , we can get:

[0027]

[0028]

[0029] in, for The first derivative of for The first derivative of for The first derivative of It is in a disturbed state.

[0030] Furthermore, the state space equation can be obtained as follows:

[0031]

[0032] in, is the input variable, , represents transpose, For input variables The first derivative of is the state transition matrix, is the input coupling matrix, is the perturbation coupling matrix, is the observation output, is the noise coupling matrix, is the disturbance signal.

[0033] Then, the state equation of the adaptive extended state observer is:

[0034]

[0035] in, is the estimated value of the state, , 、 、 are the estimated values ​​of the 1st, 2nd and 3rd dimensional states, respectively. for The first derivative of is the output estimate, is the observer gain matrix.

[0036] In order to make the observer stable and have the desired dynamic response, it is necessary to design the gain matrix The roots of the characteristic equation of the error dynamics equation are located in the left half plane and have the desired bandwidth.

[0037] Furthermore, the characteristic equation of the adaptive extended state observer gain is:

[0038] The characteristic equation of the error dynamics equation is: ; in, is the characteristic equation, is the bandwidth of the observer, is the determinant of the matrix, 、 、 is the system parameter.

[0039] Get the observer gain matrix for:

[0040] in, 、 、 is the observer gain.

[0041] Finally, the expression of the adaptive extended state observer is:

[0042]

[0043]

[0044]

[0045]

[0046]

[0047] in, 、 、 They are 、 、 The estimated value of 、 、 They are 、 、 estimated value.

[0048] Furthermore, in order to avoid the population aggregation phenomenon caused by traditional random initialization, the Chebyshev chaos map is used as the population initialization strategy. The expression of the Chebyshev chaos map is:

[0049] in, For the The input of the step iteration, For the The input of the step iteration, is the Chebyshev chaos mapping parameter, which determines the uniformity of population initialization; Adjusting the bandwidth of the observer To minimize the estimation error, The larger the value, the more conducive it is to the global search for the optimal value, but the local search ability becomes weaker; The smaller the value, the opposite is true. In order to improve the dynamics of the inertia coefficient, this paper adopts a random inertia coefficient. Specifically, the expression of the random inertia coefficient is:

[0050] in, is the dynamic frequency value, is the minimum value of the inertia coefficient, is the maximum value of the inertia coefficient, yes A random number between It is used to measure the degree of deviation between the random inertia coefficient and the expected value. is a normally distributed random number; When the population falls into a local optimum, the gravitational search algorithm and Gaussian perturbation are combined to improve the global search capability of the swarm intelligence algorithm. The expression for the combination of the gravitational search algorithm and Gaussian perturbation is:

[0051]

[0052]

[0053]

[0054]

[0055] in, For the The global best position of the iteration dimensional component, For the The global best position of the iteration dimensional component, For the The global best position of the iteration dimensional component, for arrive The absolute value of the sine of a random number within the range, yes A random number between and is the golden ratio coefficient, is the learning factor, is the scale parameter, is the time constant, and Respectively represent the maximum and minimum values ​​of each dimension of all particles in the current iteration number, is the Gaussian distribution function, is the time-varying variance.

[0056] The optimized adaptive extended state observer can estimate the electric vehicle wheel cylinder hydraulic pressure in real time to obtain the preliminary electric vehicle wheel cylinder hydraulic pressure.

[0057] Step S3, based on the adaptive extended state observer based on the improved particle swarm optimization algorithm designed in step S2, and using the estimated preliminary electric vehicle wheel cylinder hydraulic pressure as input, design a sliding mode attitude controller based on the adaptive extended state observer.

[0058] In step S3, the expression of the sliding mode attitude controller based on the adaptive extended state observer is:

[0059]

[0060]

[0061]

[0062] in, is the observation error, 、 is the observer gain.

[0063] Step S4, based on the sliding mode attitude controller based on the adaptive extended state observer designed in step S3, by improving the objective function and fitness function established by the alpha evolution algorithm, improving the sliding surface equation through the objective function and fitness function, and introducing a chaotic disturbance term, an evolutionary compensation term, and an adaptive vibration suppression factor, thereby obtaining an improved sliding mode control method, and controlling the hydraulic pressure of the wheel cylinder of the electric vehicle through the improved sliding mode control method.

[0064] In step S4, the expression of the improved alpha evolution algorithm is:

[0065]

[0066]

[0067] in, For the The updated value of the vector at the initial step, is a solution vector randomly selected from the current population, is the optimal solution vector in the current group, is the adaptive crossover probability, is the initial exploration probability, is the decay rate coefficient, is a hyperparameter, To improve the time step index of the Alpha Evolution Algorithm, is the dynamic disturbance intensity, The mean is 0 and the variance is Gaussian noise, The maximum boundary value of the value range.

[0068] Among them, the evolution equation expression of the improved Alpha evolution algorithm is:

[0069]

[0070]

[0071]

[0072]

[0073] in, For particles At the moment The velocity vector, For particles At the moment The velocity vector, For particles The individual optimal position of For the The particle location, is the global optimal position of the particle swarm, For the The particle location; is a chaotic perturbation term used to escape from the local optimum; is the chaotic perturbation function, For particles Related variables, is the inertia weight, and are the maximum inertia weight and the minimum inertia weight, is the initial amplitude of the disturbance, For the control parameters, is the attenuation coefficient, 、 is the learning factor, 、 is the adaptive constant.

[0074] Among them, the expression of the objective function is:

[0075]

[0076]

[0077] in, is the objective function, Tracking error The norm of Tracking error The first derivative of is a symbolic function, is the expected trajectory, is the actual output trajectory; is the error normalization function, which is used to suppress overcompensation when the error is large; 、 、 is the dynamic weight constant, For time The differential of is the total duration of the control process.

[0078] Among them, the expression of the fitness function is:

[0079] in, is the fitness function, is the weight coefficient, To control costs, Over time Changing control inputs.

[0080] Among them, the expression of the improved sliding surface equation is:

[0081]

[0082]

[0083]

[0084]

[0085] in, For the improved sliding surface, 、 For the The sliding surface parameters at the iteration, 、 For the The sliding surface parameters at the iteration, is the adaptive chattering suppression factor, is the error function, 、 、 is the weight factor, is the hyperbolic tangent function, is the composite control ratio, is the sliding mode control term, is the evolutionary compensation term, For the control parameters, is the stiffness coefficient, is an estimated value.

[0086] Among them, in step S4, the expression of the chaotic disturbance term is:

[0087]

[0088] in, is the chaotic perturbation term, is the coupling coefficient, is the state parameter; The expression of the evolutionary compensation term is:

[0089] in, For the weight coefficients, is the feature mapping function, is a function of time, is the total number of weight coefficients of synergistic compensation items; The expression of the adaptive chattering suppression factor is:

[0090] in, and are the minimum and maximum values ​​of the chattering suppression gain, is the natural exponential function, is the parameter that controls the gain to decay with error.

[0091] Specifically, the stability analysis is performed through the Lyapunov function. The specific formula is as follows:

[0092]

[0093] in, is the Lyapunov function, for The first derivative of is the sliding mode variable, To adjust the parameters, is an estimated value, , is the sliding mode convergence coefficient, is the estimated error convergence coefficient.

[0094] when ,and When , the system is globally asymptotically stable.

[0095] Ultimately, the hydraulic pressure of the electric vehicle wheel cylinder will be controlled through an improved sliding mode control method.

[0096] Figure 2 The comparison diagram of the wheel cylinder hydraulic pressure obtained by the method of the present invention, the hydraulic pressure obtained by the traditional sliding mode control algorithm and the ideal hydraulic pressure is shown in FIG. Figure 2 It can be seen that the curve of the method proposed in the present invention is closest to the ideal value, the response speed is shortened by 30%, and the vibration is suppressed to within 0.02MPA. It can achieve high-precision hydraulic pressure control, effectively improve the anti-interference ability and robustness, and reduce the impact of external interference on the hydraulic brake system.

[0097] In summary, the wheel cylinder hydraulic pressure control method of the electric vehicle brake-by-wire system according to the above embodiment has the following beneficial effects: (1) The present invention designs an adaptive extended state observer based on an improved particle swarm optimization algorithm, and optimizes the particle swarm optimization algorithm by combining Chebyshev chaos mapping, random inertia coefficient, gravitational search algorithm and Gaussian perturbation, which can improve the global search capability of the group.

[0098] (2) Based on the traditional hydraulic pressure control algorithm, the present invention improves the objective function and fitness function established by the Alpha evolution algorithm, improves the sliding surface equation through the objective function and fitness function, optimizes the dynamic response performance of the system, and enables the electric vehicle hydraulic brake system to remain stable when encountering external interference, significantly improving the vibration suppression capability; (3) In the sliding mode control method, the present invention introduces a chaotic disturbance term to enhance the global search capability to avoid local optimality. At the same time, the introduction of an evolutionary compensation term further improves the anti-interference ability. The adaptive vibration suppression factor can effectively weaken the vibration phenomenon of the sliding mode control, ultimately achieving high-precision and strong robustness wheel cylinder hydraulic pressure control.

[0099] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for controlling wheel cylinder hydraulic pressure in a wire-controlled brake system of an electric vehicle, characterized in that: include: Step S1, constructing a nonlinear model of the electric vehicle electronic hydraulic brake system, and establishing a brake system mathematical model, a brake system dynamics balance equation, and a brake system dynamics model based on the constructed nonlinear model; Step S2, based on the brake system mathematical model, the brake system dynamics balance equation, and the brake system dynamics model established in step S1, designing an adaptive extended state observer based on an improved particle swarm optimization algorithm to estimate the preliminary electric vehicle wheel cylinder hydraulic pressure, wherein the improved particle swarm optimization algorithm is based on the particle swarm optimization algorithm and introduces Chebyshev chaos mapping, random inertia coefficient, and a gravitational search algorithm combined with Gaussian perturbation for optimization; Step S3, based on the adaptive extended state observer based on the improved particle swarm optimization algorithm designed in step S2, and using the estimated preliminary electric vehicle wheel cylinder hydraulic pressure as input, designing a sliding mode attitude controller based on the adaptive extended state observer; Step S4, based on the sliding mode attitude controller based on the adaptive extended state observer designed in step S3, by improving the objective function and fitness function established by the alpha evolution algorithm, improving the sliding surface equation through the objective function and fitness function, and introducing a chaotic disturbance term, an evolutionary compensation term, and an adaptive vibration suppression factor, thereby obtaining an improved sliding mode control method, and controlling the hydraulic pressure of the wheel cylinder of the electric vehicle through the improved sliding mode control method.

2. The method for controlling wheel cylinder hydraulic pressure of an electric vehicle brake-by-wire system according to claim 1, characterized in that: In step S1, the mathematical model of the braking system is expressed as: in, is the equivalent moment of inertia of the rotating part, is the angular acceleration, is the rated torque of the motor, is the torque on the first-stage ball screw, is the axial thrust of the ball screw, is the ball screw lead, is the transmission efficiency, is the gear ratio; The expression of the dynamic equilibrium equation of the braking system is: in, is the wheel cylinder mass, is the equivalent damping, is the equivalent spring stiffness of the wheel cylinder, is the piston displacement, for The first derivative of for The second derivative of is the piston area, is the wheel cylinder pressure; The expression of the braking system dynamic model is: in, is the pressure change rate, is the brake chamber pressure, is the brake fluid bulk modulus, is the initial fluid volume of the brake chamber.

3. The method for controlling wheel cylinder hydraulic pressure of an electric vehicle brake-by-wire system according to claim 2, characterized in that: In step S2, the expression of Chebyshev chaos map is: in, For the The input of the step iteration, For the The input of the step iteration, is the Chebyshev chaos mapping parameter; The expression of random inertia coefficient is: in, is the dynamic frequency value, is the minimum value of the inertia coefficient, is the maximum value of the inertia coefficient, yes A random number between It is used to measure the degree of deviation between the random inertia coefficient and the expected value. is a normally distributed random number; The expression for optimizing the gravitational search algorithm combined with Gaussian perturbation is: in, For the The global best position of the iteration dimensional component, For the The global best position of the iteration dimensional component, For the The global best position of the iteration dimensional component, for arrive The absolute value of the sine of a random number within the range, yes A random number between and is the golden ratio coefficient, is the learning factor, is the scale parameter, is the time constant, and Respectively represent the maximum and minimum values ​​of each dimension of all particles in the current iteration number, is the Gaussian distribution function, is the time-varying variance.

4. The method for controlling wheel cylinder hydraulic pressure of an electric vehicle brake-by-wire system according to claim 3, characterized in that: In step S4, the expression of the improved alpha evolution algorithm is: in, For the The updated value of the vector at the initial step, is a solution vector randomly selected from the current population, is the optimal solution vector in the current group, is the adaptive crossover probability, is the initial exploration probability, is the decay rate coefficient, is a hyperparameter, To improve the time step index of the Alpha Evolution Algorithm, is the dynamic disturbance intensity, The mean is 0 and the variance is Gaussian noise, The maximum boundary value of the value range.

5. The method for controlling wheel cylinder hydraulic pressure of an electric vehicle brake-by-wire system according to claim 4, characterized in that: In step S4, the evolution equation of the improved Alpha Evolution Algorithm is expressed as: in, For particles At the moment The velocity vector, For particles At the moment The velocity vector, For particles The individual optimal position of For the The particle location, is the global optimal position of the particle swarm, For the The particle location, is the chaotic perturbation term, is the chaotic perturbation function, For particles Related variables, is the inertia weight, and are the maximum inertia weight and the minimum inertia weight, is the initial amplitude of the disturbance, is the control parameter, is the attenuation coefficient, 、 is the learning factor, 、 is the adaptive constant.

6. The method for controlling wheel cylinder hydraulic pressure of an electric vehicle brake-by-wire system according to claim 5, characterized in that: In step S4, the objective function is expressed as: in, is the objective function, Tracking error The norm of Tracking error The first derivative of is a symbolic function, is the expected trajectory, is the actual output trajectory, is the error normalization function, 、 、 is the dynamic weight constant, For time The differential of is the total duration of the control process; In step S4, the fitness function is expressed as: in, is the fitness function, is the weight coefficient, To control costs, Over time Changing control inputs.

7. The method for controlling wheel cylinder hydraulic pressure of an electric vehicle brake-by-wire system according to claim 6, characterized in that: The improved sliding surface equation is: in, For the improved sliding surface, 、 For the The sliding surface parameters at the iteration, 、 For the The sliding surface parameters at the iteration, is the adaptive chattering suppression factor, is the error function, 、 、 is the weight factor, is the hyperbolic tangent function, is the composite control ratio, is the sliding mode control term, is the evolutionary compensation term, is the control parameter, is the stiffness coefficient, is an estimated value.

8. The method for controlling wheel cylinder hydraulic pressure of an electric vehicle brake-by-wire system according to claim 7, characterized in that: In step S4, the expression of the chaotic disturbance term is: in, is the chaotic perturbation term, is the coupling coefficient, is the state parameter; The expression of the evolutionary compensation term is: in, For the weight coefficients, is the feature mapping function, is a function of time, is the total number of weight coefficients of synergistic compensation items; The expression of the adaptive chattering suppression factor is: in, and are the minimum and maximum values ​​of the chattering suppression gain, is the natural exponential function, is the parameter that controls the gain to decay with error.

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