Optimization method for control parameters of anti-lock braking system

Abstract

The invention relates to the technical field of vehicle braking, in particular to a method for optimizing control parameters of an anti-lock braking system, which comprises the following steps of: firstly, generating a virtual environment for simulating driving of a vehicle under different road conditions, and opening communication between an anti-lock braking control strategy and the virtual environment to obtain a joint simulation environment of the anti-lock braking system; and optimizing the parameter value of the anti-lock control method by adopting an intelligent optimization algorithm to obtain the optimized parameter value of the anti-lock control method, so that the response speed, the stability and the braking efficiency of the anti-lock system are remarkably improved. The anti-lock control system achieves the best braking efficiency, and therefore the best braking distance and safety are obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle braking, and more specifically, to a method for optimizing control parameters of an anti-lock braking system. Background Art

[0002] The Anti-lock Brake System (ABS) is a device that can automatically adjust the brake pressure during the braking process to prevent the wheels from locking, thereby ensuring safety and maneuverability while achieving the best braking performance. When a car is equipped with only a conventional brake system, some or all of its wheels are often locked during braking, especially during emergency braking, that is, the wheels no longer roll but slide on the ground. When the steering wheel is locked, the car will lose its response to the steering operation, making it unable to avoid obstacles, pedestrians or drive along a curve during braking; when the rear wheel is locked, the rear axle is easily disturbed by the lateral force and slides sideways, making the directional stability of the car worse during braking. In severe cases, the car will turn sharply or even turn around. In addition, when the wheel is in a locked state, the tire sliding will destroy the adhesion between it and the ground, so that the ground cannot provide enough braking force, increasing the braking distance. In short, wheel locking during braking will reduce the braking and safety performance of the car. On the contrary, installing ABS on the car to prevent the wheels from locking during braking will improve the safety performance of the car and achieve optimal braking efficiency, thereby effectively reducing traffic accidents.

[0003] The working principle of anti-lock braking system: Figure 5 As shown, the anti-lock braking system consists of a wheel speed sensor, a brake pressure regulating device, and an electronic control unit. The wheel speed sensor is used to detect the speed of the wheel and input the speed signal. The brake pressure regulating device is equipped with a solenoid valve and a hydraulic pump.

[0004] When the vehicle is in normal braking (anti-lock braking system is not working), the solenoid valve is not energized, that is, the inlet valve is open, the outlet valve is closed, and the master cylinder and the wheel cylinder are freely connected. When the brake pedal is pressed, the brake fluid in the wheel cylinder is squeezed, resulting in continuous braking. When the brake pedal is released, the brake fluid in the wheel cylinder returns to the master cylinder and is no longer squeezed. When the anti-lock braking system is working, the electronic control unit analyzes the wheel speed signal input, measures and calculates the adhesion and slip of the four wheels of the vehicle on the road surface, and controls the action of the boost solenoid valve and the decompression solenoid valve in the brake pressure regulating device through the controller of the electronic control unit, adjusts the brake oil pressure in each wheel cylinder, and makes the braking force of each wheel continuously adjusted at high speed, controls the wheel slip rate within the ideal range, and effectively utilizes the ground adhesion, so as to obtain the best braking distance and safety.

[0005] Since the current calibration of the parameters of the anti-lock braking control strategy is mostly carried out through actual tests, in order to determine each parameter, they can only work in this way: preset parameters, road test, analyze results, reset parameters... and so on, each cycle is repeated until satisfaction is achieved. This way of working can easily have an adverse effect on the efficiency, traceability and reliability of the matching work, and this work also relies heavily on the ability, experience and even responsibility of the matching engineer. Therefore, how to calibrate satisfactory anti-lock braking system control strategy parameters in a short time has become one of the basic problems that technicians in this field need to solve urgently. Summary of the invention

[0006] In order to overcome at least one of the defects of the prior art, the present invention provides a method for optimizing control parameters of an anti-lock braking system.

[0007] The present invention aims to solve the above technical problems at least to a certain extent.

[0008] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0009] A method for optimizing control parameters of an anti-lock braking system comprises the following steps:

[0010] S1: Setting virtual vehicle parameters and braking environment of the virtual vehicle;

[0011] S2: setting an anti-lock control strategy according to the virtual vehicle parameters and the braking environment of the virtual vehicle;

[0012] S3: Build a joint simulation environment including virtual vehicle parameters, braking environment of virtual vehicle and anti-lock control strategy;

[0013] S4: constructing an optimization problem, optimizing the parameter values ​​of the anti-lock braking control strategy, and obtaining optimized parameter values ​​of the anti-lock braking control strategy.

[0014] Further, step S1, setting the virtual vehicle parameters and the braking environment of the virtual vehicle, includes: setting the virtual vehicle and the anti-lock braking test condition parameters of the virtual vehicle.

[0015] Further, in step S3, a joint simulation environment including a virtual vehicle, a braking environment of the virtual vehicle and an anti-lock control strategy is constructed, including:

[0016] The anti-lock control strategy receives a signal from a virtual vehicle, calculates the slip rate and wheel acceleration of the vehicle wheels, and controls the wheel cylinder pressure of the vehicle according to the slip rate and wheel acceleration, thereby controlling the braking state of each wheel.

[0017] Furthermore, the braking state of each wheel includes a pressure-increasing state, a pressure-reducing state and a pressure-maintaining state, and the pressure-increasing state includes an initial pressure-increasing state and a step-by-step pressure-increasing state.

[0018] Further, the optimization variables of the optimization problem are parameter values ​​of the anti-lock control strategy, and the parameter values ​​of the anti-lock control strategy include the slip rate threshold of each wheel entering decompression for the first time, the slip rate threshold from maintaining pressure to entering step supercharging, the slip rate threshold from step supercharging to entering decompression, the wheel acceleration threshold entering the first decompression, the wheel acceleration threshold from decompression to maintaining pressure, and the wheel acceleration threshold from maintaining pressure to entering decompression.

[0019] Further, in step S2, the anti-lock braking control strategy includes the following steps:

[0020] S2.1: The wheel enters the first boost state after receiving the braking signal;

[0021] S2.2: If the slip rate of the current wheel is greater than the slip rate threshold of the first decompression state or the acceleration of the current wheel is less than the wheel acceleration threshold of the first decompression state, the wheel enters the decompression state;

[0022] S2.3: If the current slip rate is not greater than the slip rate threshold for the first decompression or the current wheel acceleration is not less than the wheel acceleration threshold for the first decompression, the wheel maintains the first pressurization state;

[0023] S2.4: If the wheel acceleration in the decompression state is less than the wheel acceleration threshold for entering the pressure-maintaining state from decompression, the wheel enters the pressure-maintaining state; otherwise, the wheel remains in the decompression state;

[0024] S2.5: If the wheel acceleration in the pressure-maintaining state is less than the wheel acceleration threshold for entering the pressure-reducing state from the pressure-maintaining state, the wheel enters the pressure-reducing state, otherwise jump to step 2.6;

[0025] S2.6: If the wheel acceleration in the pressure-maintaining state is not less than the wheel acceleration threshold for pressure-maintaining to enter decompression, then compare the slip rate of the wheel with the slip rate threshold for pressure-maintaining to enter step-boosting. If the slip rate of the wheel is greater than the slip rate threshold for pressure-maintaining to enter step-boosting, then the wheel enters the step-boosting state; otherwise, maintain the pressure-maintaining state.

[0026] S2.7: If the wheel slip rate in the step-boost state is greater than the wheel slip rate threshold for step-boost to decompression, the wheel enters the decompression state, otherwise the step-boost state is maintained.

[0027] Further, in step S4, the optimization of the parameter value of the anti-lock braking control strategy includes:

[0028] An optimization algorithm is used to search for the minimum value of the objective function, and the minimum value of the objective function that meets the constraints is output. The parameter value of the optimized anti-lock control strategy is obtained according to the minimum value of the objective function that meets the constraints.

[0029] Further, the objective function is the square of the difference between the vehicle braking distance and the expected braking distance, including:

[0030]

[0031] In the formula, BD real is the actual braking distance, BD ideal is the ideal braking distance, V x is the braking speed at position x, t i is the i-th time point, t f is the fth time point.

[0032] Furthermore, the constraints include:

[0033] The slip rate threshold for entering the first decompression> the slip rate threshold for entering the step-by-step boost after maintaining the pressure> the slip rate threshold for entering the decompression after the step-by-step boost> 0;

[0034] The wheel acceleration threshold for entering the first decompression is less than the wheel acceleration threshold for entering the pressure maintenance after decompression;

[0035] The wheel acceleration threshold from pressure maintenance to pressure reduction is less than the wheel acceleration threshold from pressure reduction to pressure maintenance;

[0036] The wheel acceleration threshold for entering the first decompression is <0;

[0037] The wheel acceleration threshold from decompression to pressure maintenance is > 0;

[0038] The wheel acceleration threshold for maintaining pressure and entering decompression is > 0.

[0039] Further, in step S4, the parameter values ​​of the anti-lock braking control strategy are optimized to obtain the optimized parameter values ​​of the anti-lock braking control strategy, including:

[0040] S4.1: Programming the objective function and constraints;

[0041] S4.2: Initialize the genetic algorithm and set the population size;

[0042] S4.3: Calculate the objective function value for all individuals in the population and save the minimum value of the objective function among all individuals;

[0043] S4.4: The individuals whose objective function values ​​are less than the preset threshold are replicated, and the individuals whose objective function values ​​are greater than the preset threshold are eliminated;

[0044] S4.5: Perform random crossover and mutation processing on individuals whose objective function values ​​are less than the preset threshold to obtain a new population;

[0045] S4.6: Calculate the objective function values ​​of all individuals in the new population, save the lowest objective function value among all individuals as the current lowest objective function value; if the termination condition is met, output the individual corresponding to the minimum objective function value to obtain the optimized anti-lock control strategy parameter value, otherwise, return the new generation population to step S4.4.

[0046] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0047] The anti-lock braking control strategy parameter formulation method uses a genetic algorithm to find the optimal parameters of the optimal anti-lock braking control strategy, accelerates the parameter calibration process of the anti-lock braking system, and can effectively reduce the parameter formulation cost, improve work efficiency, and improve vehicle braking performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flow chart of the method for formulating anti-lock braking control strategy parameters according to the present invention;

[0049] Figure 2 A schematic diagram of the logic threshold control logic of the anti-lock braking system of the present invention;

[0050] Figure 3 This is a structural diagram of the joint simulation environment described in the present invention;

[0051] Figure 4 A flowchart of the operation of the genetic algorithm of the present invention;

[0052] Figure 5 is a module block diagram of the anti-lock braking system described in this embodiment;

[0053] Figure 6 This is a flow chart of parameter optimization of the anti-lock braking system control method described in this embodiment. DETAILED DESCRIPTION

[0054] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0055] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0056] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0057] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0058] Example 1

[0059] A method for optimizing control parameters of an anti-lock braking system, such as Figure 1 As shown, the following steps are included:

[0060] S1: Setting the virtual vehicle and the braking environment of the virtual vehicle;

[0061] S2: setting the anti-lock control strategy according to the virtual vehicle and the braking environment of the virtual vehicle;

[0062] S3: Build a joint simulation environment including a virtual vehicle, its braking environment and anti-lock control strategy;

[0063] S4: constructing an optimization problem, optimizing the parameter values ​​of the anti-lock braking control strategy, and obtaining optimized parameter values ​​of the anti-lock braking control strategy.

[0064] In the specific implementation process, a joint simulation environment including a virtual vehicle, a braking environment of the virtual vehicle and an anti-lock control strategy is built, an optimization problem with parameter values ​​of the anti-lock control strategy as optimization variables is constructed, and an intelligent optimization algorithm is used to solve the optimization problem. After multiple optimization iterations, an individual with the smallest objective function value in the population in the last iteration is taken out, and the optimization variables contained in the individual are the final optimization parameters of the anti-lock control strategy parameters. The present invention can calibrate the braking anti-lock braking system control strategy parameters in a relatively short time.

[0065] Example 2

[0066] This embodiment further discloses the following contents based on the embodiment 1:

[0067] Step S1, setting the virtual vehicle and the braking environment of the virtual vehicle, includes: setting the virtual vehicle parameters and the anti-lock braking test condition parameters of the virtual vehicle.

[0068] In the specific implementation process, setting the virtual vehicle and the braking environment of the virtual vehicle includes: using Carsim software to set the dynamic parameters and anti-lock control test conditions of the virtual vehicle to simulate the braking conditions in the vehicle driving environment.

[0069] Carsim software simulates the vehicle's response to driver input, road input and aerodynamic input, and conveniently models the vehicle body, aerodynamics, power system, braking system, steering system, tires and suspension, and then sets test conditions to evaluate the vehicle's handling stability, power, economy, braking and smoothness.

[0070] Step S3, the building of a joint simulation environment including a virtual vehicle, a braking environment of the virtual vehicle and an anti-lock braking control strategy, comprises:

[0071] The anti-lock control strategy receives a signal from a virtual vehicle, calculates the slip rate and wheel acceleration of the vehicle wheels, and controls the wheel cylinder pressure of the vehicle according to the slip rate and wheel acceleration, thereby controlling the braking state of each wheel.

[0072] The braking state of each wheel includes a pressure-increasing state, a pressure-reducing state and a pressure-maintaining state, and the pressure-increasing state includes an initial pressure-increasing state and a step-by-step pressure-increasing state.

[0073] In the specific implementation process, the virtual vehicle and the braking environment of the virtual vehicle built in step S1 are generated into an S-Function block and sent to Simulink, and connected with the corresponding interface of the Simulink anti-lock control strategy built in step S2 to build a joint simulation environment of the virtual vehicle, the virtual test environment and the anti-lock control strategy, such as Figure 3 shown.

[0074] In the Carsim software, first fill in the relevant vehicle dynamics parameters and parameters of the virtual braking condition; then use the Simulink software to create a Simulink (.slx or .mdl) file, and fill in the path of this file in the Carsim software and Simulink joint simulation interface; next, set up the input and output interfaces in the Carsim software, and click the Send to Simulink button in the RunControl interface of the Carsim software to place the S-Function block generated by the Carsim software in the Simulink model, realize the joint simulation of the Carsim software and Simulink, and then complete the construction of the joint simulation environment of the anti-lock control strategy, virtual vehicle and virtual braking environment.

[0075] Table 1 Anti-lock braking system test conditions

[0076]

[0077]

[0078] Table 1 gives the parameters of 11 virtual braking conditions, which can be divided into the following four groups:

[0079] The first set of operating condition data is a smooth road with a constant friction coefficient. The purpose of this set of operating conditions is to evaluate the performance of the anti-lock braking system on a road with a constant friction coefficient. The three friction coefficient levels correspond to three different road surfaces. The friction coefficient of 1.0 corresponds to a dry asphalt road surface, the friction coefficient of 0.8 corresponds to a wet asphalt road surface, and the friction coefficient of 0.3 corresponds to a snowy road surface.

[0080] The second set of working condition data is the docking road surface. The purpose of this set of working conditions is to evaluate the transient response of the controller. There are three different docking road surfaces, namely, the friction coefficient switches from 1.0 to 0.8, representing dry asphalt road surface to wet asphalt road surface; the friction coefficient switches from 0.8 to 0.3, representing wet asphalt road surface to snowy road surface; the friction coefficient switches from 0.3 to 0.8, representing snowy road surface to wet asphalt road surface.

[0081] The third set of working condition data is for a split road surface. The purpose of this set of working conditions is to evaluate the impact of the anti-lock braking system on vehicle stability. There are three different split road surfaces: the left side has a friction coefficient of 1.0, and the right side has a friction coefficient of 0.8, corresponding to dry asphalt on the left and wet asphalt on the right; the left friction coefficient is 0.8, and the right friction coefficient is 0.3, corresponding to wet asphalt on the left and snow on the right; the left friction coefficient is 0.3, and the right friction coefficient is 0.8, corresponding to snow on the left and wet asphalt on the right.

[0082] The fourth set of operating condition data is for washboard road conditions. Because the frequency and amplitude depend on the shape of the road, this type of noise is generally difficult to filter out. The purpose of this set of operating conditions is to evaluate the performance degradation of the anti-lock braking system when encountering high-frequency noise.

[0083] It should be pointed out that the simulation conditions are not limited to the above conditions, and the corresponding conditions can be determined according to actual development needs.

[0084] Example 3

[0085] This embodiment further discloses the following contents based on Embodiments 1 and 2:

[0086] The optimization variables of the optimization problem are parameter values ​​of the anti-lock control strategy, and the parameter values ​​of the anti-lock control strategy include the slip rate threshold (slip1) for first entering decompression, the slip rate threshold (slip2) for entering step supercharging from maintaining pressure, the slip rate threshold (slip3) for entering decompression from step supercharging, the wheel acceleration threshold (accel1) for entering the first decompression, the wheel acceleration threshold (accel2) for entering maintaining pressure from decompression, and the wheel acceleration threshold (accel3) for entering decompression from maintaining pressure.

[0087] The anti-lock control strategy is set, including building a vehicle anti-lock control strategy module in Simulink, wherein the anti-lock control strategy receives vehicle braking commands, vehicle speed, and wheel speed from a virtual vehicle and a virtual test environment, calculates the slip rate and wheel acceleration of each wheel of the vehicle based on the information, controls the vehicle wheel cylinder pressure based on the slip rate and wheel acceleration, sends the wheel cylinder pressure to the virtual vehicle, and then controls the braking state of each wheel.

[0088] Step S2, the logic of the anti-lock control strategy, such as Figure 2 As shown, the following steps are included:

[0089] S2.1: The wheel enters the first boost state after receiving the braking signal;

[0090] S2.2: If the slip rate of the current wheel is greater than the slip rate threshold (slip1) of the first decompression state or the acceleration of the current wheel is less than the wheel acceleration threshold (accel1) of the first decompression state, the wheel enters the decompression state;

[0091] S2.3: If the current slip rate is not greater than the slip rate threshold for the first decompression or the current wheel acceleration is not less than the wheel acceleration threshold for the first decompression, the wheel maintains the first pressurization state;

[0092] S2.4: If the wheel acceleration in the decompression state is less than the wheel acceleration threshold (accel2) for entering the pressure-maintaining state from decompression, the wheel enters the pressure-maintaining state; otherwise, the wheel remains in the decompression state;

[0093] S2.5: If the wheel acceleration in the pressure-maintaining state is less than the wheel acceleration threshold (accel3) for entering the pressure-reducing state from the pressure-maintaining state, the wheel enters the pressure-reducing state, otherwise, the process goes to step 2.6;

[0094] S2.6: If the wheel acceleration in the pressure-maintaining state is not less than the wheel acceleration threshold (accel3) for pressure-maintaining to decompression, then compare the slip rate of the wheel with the slip rate threshold for pressure-maintaining to step-up. If the slip rate of the wheel is greater than the slip rate threshold (slip2) for pressure-maintaining to step-up, then the wheel enters the step-up state, otherwise, maintain the pressure-maintaining state.

[0095] S2.7: If the wheel slip rate in the step-boost state is greater than the wheel slip rate threshold (slip3) for step-boost to decompression, the wheel enters the decompression state; otherwise, the step-boost state is maintained.

[0096] Example 4

[0097] This embodiment further discloses the following contents based on Embodiments 1, 2 and 3:

[0098] Step S4, constructing an optimization problem, including:

[0099] The optimization variables of the optimization problem are parameter values ​​of the anti-lock control strategy, which include the slip rate threshold of each wheel entering decompression for the first time, the slip rate threshold from maintaining pressure to step supercharging, the slip rate threshold from step supercharging to decompression, the wheel acceleration threshold entering the first decompression, the wheel acceleration threshold from decompression to maintaining pressure, and the wheel acceleration threshold from maintaining pressure to decompression.

[0100] In the specific implementation process, the anti-lock braking control strategy is analyzed to extract the parameters that need to be continuously adjusted and optimized in the anti-lock braking control strategy, that is, the optimization variables of the optimization problem.

[0101] The optimization variables are 6 front wheel slip rate threshold values, 3 for each left and right wheel, 6 rear wheel slip rate threshold values, 3 for each left and right wheel, 6 front wheel acceleration threshold values, 3 for each left and right wheel, and 6 rear wheel acceleration threshold values, 3 for each left and right wheel, totaling 24 optimization variables.

[0102] Example 5

[0103] This embodiment, based on Embodiments 1, 2, 3 and 4, continues to disclose the following contents:

[0104] The objective function is the square of the difference between the vehicle braking distance and the expected braking distance, including:

[0105]

[0106] In the formula, BD real is the actual braking distance, BD ideal is the ideal braking distance, V x is the braking speed at position x, t i is the i-th time point, t f is the fth time point.

[0107] In the specific implementation process, different evaluation indicators are set according to the corresponding working conditions to evaluate the working performance of the anti-lock braking system according to different test conditions, and the square of the difference between the evaluation index value and the expected evaluation index value is used as the objective function of the subsequent intelligent optimization algorithm. Several commonly used evaluation indicators are given below:

[0108] Braking distance (BD): the time t when the brake pedal is pressed i When the exit speed is reached, f The distance travelled.

[0109]

[0110] Mean braking deceleration (MFDD): The vehicle is at 90% of the initial speed v 0 The average value of the vehicle deceleration from zero to 5% of initial speed.

[0111]

[0112] Maximum yaw rate

[0113]

[0114] The braking efficiency of the anti-lock braking system (η ABS ): average longitudinal deceleration and theoretical maximum longitudinal deceleration Ratio.

[0115]

[0116] Time multiplied by absolute jerk |J x |Integral(ITAE Jx ):

[0117]

[0118] Integrated Pitch Variation (IPV):

[0119]

[0120] Recovery time (T rec ): The time required for the front wheel to pass through the road surface for the first time and the friction coefficient changes until the longitudinal deceleration stabilizes to a certain range of values.

[0121]

[0122] Average deceleration when the friction coefficient changes The average deceleration during this period is calculated from the time when the front wheel passes through the change in the friction coefficient of the road surface to 1 second after the rear wheel passes through the change in the friction coefficient of the road surface.

[0123]

[0124] Maximum yaw rate when friction coefficient changes The maximum yaw rate during this period is calculated from the time when the front wheel passes through the change in the friction coefficient of the road surface to 1 second after the rear wheel passes through the change in the friction coefficient of the road surface.

[0125]

[0126] For roads with a constant friction coefficient or rough roads, the evaluation indicators can be braking distance, braking deceleration, maximum yaw rate, braking efficiency, time multiplied by the integral of absolute jerk, and pitch angle change integral. For butted roads, the evaluation indicators are recovery time, average deceleration when the friction coefficient changes, and maximum yaw rate when the friction coefficient changes. For split roads, the evaluation indicators are braking distance, average braking deceleration, and maximum yaw rate. It should be pointed out that the evaluation indicators are not limited to the above indicators, and the corresponding indicators are determined according to actual development needs.

[0127] The constraint relationship between various parameters of the anti-lock control strategy in the present invention is analyzed. A control strategy based on slip rate and acceleration is selected in the present invention. According to the relationship between brake pressure, wheel speed and ground adhesion, each anti-lock control cycle is divided into three states: pressure increase, pressure reduction and pressure maintenance. Due to the particularity of the first cycle, the pressure increase state is further divided into the first pressure increase and step pressure increase. The initial parameter of the threshold value is the empirical value summarized in the actual vehicle road test.

[0128] In the anti-lock control strategy, the first cycle should allow the anti-lock function to be fully utilized, requiring a large speed difference, but not too strong braking. Too strong or too weak anti-lock cycles will bring disadvantages to subsequent anti-lock cycles, so the first boost state should have a larger slip rate threshold value (slip1) and a smaller acceleration threshold value (accel1). The initial stage of braking is in the pressure building stage, and the boost rate should be faster than the rate in the later stage of braking, so that sufficient pressure can be built up in the initial stage of braking to ensure that the brake will not loosen in the later stage.

[0129] The decompression state causes the wheel speed to rise. If the acceleration threshold value (accel2) is set too small, the wheel speed will not fully recover before entering the pressure-maintaining state, which will cause a locking tendency. If the acceleration threshold value (accel2) is set too large, the wheel speed may not reach this threshold value. In this case, it is possible to lose control, causing the wheel speed to remain in the decompression recovery state, and the braking time and distance will be longer.

[0130] In the pressure-maintaining state, if the wheel acceleration is less than the acceleration threshold value (accel3), it means that the wheel speed has not recovered enough at this time. Continuing to maintain or increase the pressure may cause the wheel to lock. The decompression state should be entered again to appropriately increase the wheel speed. On the other hand, in order to prevent the wheel speed from recovering excessively, a certain braking state needs to be maintained until the slip rate is less than the slip rate threshold value (slip2) and the step-by-step pressure-increasing state is entered.

[0131] Compared with the first supercharging state, the supercharging rate of the step supercharging state is smaller than that of the first supercharging state to ensure that the wheel slip rate does not increase rapidly. When the wheel slip rate is greater than the slip rate threshold value (slip3), it means that the wheel slip rate is too large and it is necessary to enter the decompression state again.

[0132] In the actual control process, since the driving wheels have a larger moment of inertia than the non-driving wheels, under the same threshold, the non-driving wheels are more sensitive to braking pressure, so the threshold value needs to be smaller. The front wheels are driving wheels, so it is necessary to ensure that the front wheel threshold value is greater than the rear wheel threshold value.

[0133] Based on the above analysis, the constraints between the optimization variables of the optimization problem are:

[0134] The slip rate threshold for entering the first decompression (slip1)> the slip rate threshold for entering the step-up pressure from maintaining pressure (slip2)> the slip rate threshold for entering the decompression from the step-up pressure (slip3)>0;

[0135] The wheel acceleration threshold for entering the first decompression (accel1) is less than the wheel acceleration threshold for entering the pressure maintenance from decompression (accel2);

[0136] The wheel acceleration threshold from pressure maintenance to pressure reduction (accel3) is less than the wheel acceleration threshold from pressure reduction to pressure maintenance (accel2);

[0137] The wheel acceleration threshold for entering the first decompression (accel1) is less than 0;

[0138] The wheel acceleration threshold from decompression to pressure maintenance (accel2)>0;

[0139] The wheel acceleration threshold (accel3) for maintaining pressure and entering decompression is greater than 0.

[0140] Example 6

[0141] This embodiment, based on Embodiments 1, 2, 3, 4 and 5, continues to disclose the following contents:

[0142] In step S4, the parameter values ​​of the anti-lock braking control strategy are optimized to obtain the optimized parameter values ​​of the anti-lock braking control strategy, such as Figure 4 As shown, the following steps are included:

[0143] S4.1: Programming the objective function and constraints;

[0144] S4.2: Initial genetic algorithm, set population size;

[0145] S4.3: Calculate the objective function value for all individuals in the population and save the minimum value of the objective function among all individuals;

[0146] S4.4: The individuals whose objective function values ​​are less than the preset threshold are replicated, and the individuals whose objective function values ​​are greater than the preset threshold are eliminated;

[0147] S4.5: Perform random crossover and mutation processing on individuals whose objective function values ​​are less than the preset threshold to obtain a new population;

[0148] S4.6: Calculate the objective function values ​​of all individuals in the new population, save the lowest objective function value among all individuals as the current lowest objective function value; if the termination condition is met, output the individual corresponding to the minimum objective function value to obtain the optimized anti-lock control strategy parameter value, otherwise, return the new generation population to step S4.4.

[0149] In the specific implementation process, the termination condition is set to output the individual corresponding to the minimum objective function value until the objective function value no longer shows a downward trend, because in some steps, the objective function value may occasionally rise, but the overall trend is still downward.

[0150] In step S4.3, the objective function value is calculated for all individuals in the population, and the minimum value of the objective function among all individuals is saved, including:

[0151] S4.31: Each individual in the population is provided with an optimization variable value corresponding to the individual, that is, a parameter value of the anti-lock control strategy;

[0152] S4.32: For each individual in the population, run the joint simulation environment, calculate the objective function value corresponding to the individual based on the result of the joint simulation, and save it in the individual;

[0153] S4.33: Compare the objective function values ​​corresponding to each individual in the population, and obtain the lowest objective function value among all individuals as the final objective function value of this iteration.

[0154] Example 7

[0155] This embodiment, based on Embodiments 1, 2, 3, 4, 5 and 6, continues to disclose the following contents:

[0156] In the specific implementation process, Figure 6 As shown, set the virtual vehicle parameters and virtual braking conditions in Carsim software; build the anti-lock control strategy logic in Simulink; build a joint simulation environment in Simulink; use Matlab scripts to run the joint simulation environment, and optimize the parameters of the anti-lock control strategy.

[0157] Step 1: The vehicle dynamics part in Carsim software is set according to the real parameters obtained from the actual vehicle test. According to the anti-lock control strategy described in the present invention, the input and output interfaces of Carsim software and Simulink are set. The braking command, vehicle speed, acceleration of four wheels and other signals required by the anti-lock control strategy are sent by Carsim software to Simulink, and Simulink sends the brake wheel cylinder pressure signal to Carsim software. The actual test condition is set as a linear deceleration condition on a dry asphalt road surface, which is set in Carsim software. The specific settings are: the initial vehicle speed is 90km / h, the road friction coefficient is 0.8, the test condition exit speed is 5km / h, and the braking distance is selected as the evaluation index of the anti-lock braking system.

[0158] Step 2: Build the aforementioned anti-lock braking control strategy in Simulink and connect it to the Carsim interface to build the Carsim-Simulink joint simulation environment, and use the parameters of the aforementioned anti-lock braking control strategy as the optimization variables of the subsequent intelligent optimization algorithm.

[0159] Step 3: Use m language to program the objective function that needs to be optimized in Matlab. The input of the function is the parameters of the classic logic threshold anti-lock control strategy (slip1, slip2, slip3, accel1, accel2, accel3). The function body first assigns the input of the function to the parameters of the anti-lock control strategy module in the joint simulation environment, and then runs the joint simulation. Based on the simulation results, the objective function is calculated as the return value of the function. Use m language to program the constraint function that needs to be optimized. The input of the function is the parameters of the logic threshold anti-lock control strategy (slip1, slip2, slip3, accel1, accel2, accel3), and the output of the function is the penalty value corresponding to the violation of the constraint.

[0160] Step 4: Use the m language to encode the intelligent optimization algorithm in Matlab. Take the classic genetic algorithm as an example. The individual is the anti-lock control strategy parameter that needs to be optimized. Encode the optimization variable value of the individual by 01, set the population size, set the objective function to the Matlab function encoded in Step 3, and set the constraint function to the Matlab function encoded in Step 3. Run the intelligent optimization algorithm. In each iteration of the algorithm, perform the following steps: evaluate the objective function value of each individual (i.e., run the optimization objective function encoded in Step 3), select, cross, and mutate between individuals, and then select a group of solutions with relatively small objective function values ​​as the initial solution for the next iteration. Keep looping until the objective function value no longer decreases. Take out the individual with the smallest objective function value in the population in the last iteration, and the optimization variable contained in this individual is the final solution.

[0161] The same or similar reference numerals correspond to the same or similar components;

[0162] The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent;

[0163] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for optimizing control parameters of an anti-lock braking system, characterized in that: The following steps are involved: S1: Setting virtual vehicle parameters and braking environment of the virtual vehicle; S2: setting an anti-lock control strategy according to the virtual vehicle parameters and the braking environment of the virtual vehicle; S3: Build a joint simulation environment including virtual vehicle parameters, braking environment of virtual vehicle and anti-lock control strategy; S4: constructing an optimization problem, optimizing the parameter values ​​of the anti-lock braking control strategy, and obtaining optimized parameter values ​​of the anti-lock braking control strategy.

2. The method for optimizing control parameters of an anti-lock braking system according to claim 1, characterized in that: Step S1, setting virtual vehicle parameters and a braking environment of the virtual vehicle, includes: setting a virtual vehicle and anti-lock braking test condition parameters of the virtual vehicle.

3. The method for optimizing control parameters of an anti-lock braking system according to claim 2, characterized in that: Step S3, building a joint simulation environment including a virtual vehicle, a braking environment of the virtual vehicle, and an anti-lock control strategy, including: The anti-lock control strategy receives a signal from a virtual vehicle, calculates the slip rate and wheel acceleration of the vehicle wheels, and controls the wheel cylinder pressure of the vehicle according to the slip rate and wheel acceleration, thereby controlling the braking state of each wheel.

4. The method for optimizing control parameters of an anti-lock braking system according to claim 3, characterized in that: The braking state of each wheel includes a pressure-increasing state, a pressure-reducing state and a pressure-maintaining state, and the pressure-increasing state includes an initial pressure-increasing state and a step-by-step pressure-increasing state.

5. The method for optimizing control parameters of an anti-lock braking system according to claim 1, characterized in that: The optimization variables of the optimization problem are parameter values ​​of the anti-lock control strategy, which include the slip rate threshold of each wheel entering decompression for the first time, the slip rate threshold from maintaining pressure to step supercharging, the slip rate threshold from step supercharging to decompression, the wheel acceleration threshold entering the first decompression, the wheel acceleration threshold from decompression to maintaining pressure, and the wheel acceleration threshold from maintaining pressure to decompression.

6. The method for optimizing control parameters of an anti-lock braking system according to claim 3, characterized in that: In step S2, the anti-lock braking control strategy includes the following steps: S2.1: The wheel enters the first boost state after receiving the braking signal of the virtual vehicle; S2.2: If the slip rate of the current wheel is greater than the slip rate threshold of the first decompression state or the acceleration of the current wheel is less than the wheel acceleration threshold of the first decompression state, the wheel enters the decompression state; S2.3: If the current slip rate is not greater than the slip rate threshold for the first decompression or the current wheel acceleration is not less than the wheel acceleration threshold for the first decompression, the wheel maintains the first pressurization state; S2.4: If the wheel acceleration in the decompression state is less than the wheel acceleration threshold for entering the pressure-maintaining state from decompression, the wheel enters the pressure-maintaining state; otherwise, the wheel remains in the decompression state; S2.5: If the wheel acceleration in the pressure-maintaining state is less than the wheel acceleration threshold for entering the pressure-reducing state from the pressure-maintaining state, the wheel enters the pressure-reducing state, otherwise jump to step 2.6; S2.6: If the wheel acceleration in the pressure-maintaining state is not less than the wheel acceleration threshold for pressure-maintaining to enter decompression, then compare the slip rate of the wheel with the slip rate threshold for pressure-maintaining to enter step-boosting. If the slip rate of the wheel is greater than the slip rate threshold for pressure-maintaining to enter step-boosting, then the wheel enters the step-boosting state; otherwise, maintain the pressure-maintaining state. S2.7: If the wheel slip rate in the step-boost state is greater than the wheel slip rate threshold for step-boost to decompression, the wheel enters the decompression state, otherwise the step-boost state is maintained.

7. The method for optimizing control parameters of an anti-lock braking system according to claim 5, characterized in that: Step S4, optimizing the parameter values ​​of the anti-lock braking control strategy, including: A genetic algorithm is used to search for the minimum value of the objective function, and the minimum value of the objective function that meets the constraints is output. The parameter value of the optimized anti-lock control strategy is obtained according to the minimum value of the objective function that meets the constraints.

8. The method for optimizing control parameters of an anti-lock braking system according to claim 7, characterized in that: The objective function is the square of the difference between the vehicle braking distance and the expected braking distance, including: In the formula, BD real is the actual braking distance, BD ideal is the ideal braking distance, V x is the braking speed at position x, t i is the i-th time point, t f is the fth time point.

9. The method for optimizing control parameters of an anti-lock braking system according to claim 7, characterized in that: The constraints include: The slip rate threshold for entering the first decompression> the slip rate threshold for entering the step-by-step boost after maintaining the pressure> the slip rate threshold for entering the decompression after the step-by-step boost> 0; The wheel acceleration threshold for entering the first decompression is less than the wheel acceleration threshold for entering the pressure maintenance after decompression; The wheel acceleration threshold from pressure maintenance to pressure reduction is less than the wheel acceleration threshold from pressure reduction to pressure maintenance; The wheel acceleration threshold for entering the first decompression is <0; The wheel acceleration threshold from decompression to pressure maintenance is > 0; The wheel acceleration threshold for maintaining pressure and entering decompression is >0.

10. The method for optimizing control parameters of an anti-lock braking system according to claim 9, characterized in that: In step S4, the parameter values ​​of the anti-lock braking control strategy are optimized to obtain the optimized parameter values ​​of the anti-lock braking control strategy, including: S4.1: Programming the objective function and constraints; S4.2: Initialize the genetic algorithm and set the population size; S4.3: Calculate the objective function value for all individuals in the population and save the minimum value of the objective function among all individuals; S4.4: The individuals whose objective function values ​​are less than the preset threshold are replicated, and the individuals whose objective function values ​​are greater than the preset threshold are eliminated; S4.5: Perform random crossover and mutation processing on individuals whose objective function values ​​are less than the preset threshold to obtain a new population; S4.6: Calculate the objective function values ​​of all individuals in the new population, save the lowest objective function value among all individuals as the current lowest objective function value; if the termination condition is met, output the individual corresponding to the minimum objective function value to obtain the optimized anti-lock control strategy parameter value, otherwise, return the new generation population to step S4.4.

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