IBC system test and parameter optimization system and method for multi-road-condition simulation

Abstract

The invention discloses an IBC system test and parameter optimization system and method for multi-road-condition simulation. The IBC system test and parameter optimization system is composed of a brake system test platform and a parameter optimization control system. The brake system test platform is used for simulating physical conditions corresponding to test road conditions, testing a vehicle brake system, obtaining control signals output by the vehicle simulation model and collecting test data of the vehicle brake system; and the parameter optimization control system is used for sending a test instruction to the brake system test platform, optimizing IBC system control parameters according to test data obtained by the vehicle brake system, and finally feeding back a parameter optimization result to the brake system test platform for effect verification. Various complex actual road conditions can be flexibly simulated, control parameters of the IBC system are automatically optimized according to real-time road condition simulation and vehicle response, and therefore efficient debugging and optimization of the IBC system are achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of automobile chassis control system testing, and relates to an IBC system testing and parameter optimization method, and specifically to an IBC system testing and parameter optimization system and method for multi-road condition simulation. Background Art

[0002] In existing vehicle chassis control system tests, especially those for IBC (Integrated Braking Control) systems, traditional test benches can usually only simulate limited standard road conditions, such as flat roads, slippery roads, etc. The limitation of this test method is that it cannot fully verify the performance of the IBC system in complex and dynamically changing actual road conditions.

[0003] In practical applications, vehicles need to face a variety of complex road conditions, especially the responsiveness and stability under different driving conditions (such as emergency braking, hill start, icy and snowy roads, slippery and potholes, etc.). Therefore, the existing test equipment cannot fully meet the needs of modern automobile chassis systems for simulating variable road conditions, which poses new challenges to the testing and comprehensive optimization of IBC systems. Summary of the invention

[0004] In order to solve the above-mentioned problems existing in the prior art, the present invention provides an IBC system testing and parameter optimization system and method for multi-road condition simulation, which combines a braking system test platform and a parameter optimization control system, flexibly simulates various complex actual road conditions, and automatically optimizes the control parameters of the IBC system according to real-time road condition simulation and vehicle response, thereby realizing efficient debugging and optimization of the IBC system, which can greatly improve the reliability and controllability of the IBC system under complex driving conditions.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides an IBC system test and parameter optimization system for multi-road condition simulation, which is composed of a brake system test platform and a parameter optimization control system;

[0007] The brake system test platform is used to simulate the physical conditions corresponding to the test road conditions, conduct vehicle brake system tests, obtain control signals output by the vehicle simulation model, and collect test data of the vehicle brake system;

[0008] The parameter optimization control system is used to send test instructions to the brake system test platform, and optimize the IBC system control parameters according to the test data obtained from the vehicle brake system. Finally, the parameter optimization results are fed back to the brake system test platform for effect verification.

[0009] Furthermore, the brake system test platform comprises:

[0010] A multi-axis drive test bench equipped with a brake pedal, brake controller, brake caliper and hydraulic circuit of a vehicle braking system; based on the control signal of the test bench controller, it simulates the physical conditions of different road conditions and the execution action of the vehicle braking system;

[0011] Sensor assembly, used to collect physical parameters of the vehicle braking system during the test in real time;

[0012] The test bench controller is used to refer the road condition simulation model to the vehicle simulation model to obtain the control signal output by the vehicle simulation model; send posture control instructions to the multi-axis drive test bench to simulate the physical conditions corresponding to the test road conditions and the execution actions of the vehicle braking system, obtain the physical parameters collected by the sensor assembly and the vehicle status signal output by the vehicle simulation model, and send the test data to the parameter optimization control system.

[0013] Furthermore, the physical conditions for simulating different road conditions include slope, friction coefficient, and degree of bumpiness.

[0014] Furthermore, the sensor assembly includes a wheel speed sensor, an accelerometer, a gyroscope and a braking force sensor.

[0015] Furthermore, the parameter optimization control system comprises:

[0016] A road condition database, used to establish and store road condition simulation models of various typical road conditions, and select or establish a road condition simulation model according to test requirements and send it to the bench controller of the brake system test platform;

[0017] A data interaction module, used for performing data interaction with the brake system test platform, sending test control instructions and road condition simulation model data to the brake system test platform, and receiving test data sent by the brake system test platform;

[0018] The IBC system parameter dynamic optimization module optimizes the IBC system parameters through genetic algorithms based on the test data, and feeds the optimization results back to the brake system test platform, repeating multiple rounds of testing and optimization.

[0019] Furthermore, the typical road conditions include urban roads, expressways, and rural roads.

[0020] Furthermore, the IBC system parameter dynamic optimization module includes:

[0021] Initialization unit, used to define optimization parameters, and perform parameter encoding and population initialization;

[0022] Fitness evaluation unit, used to evaluate the fitness of IBC under various parameter settings;

[0023] The reproduction and mutation unit is used to select the individuals with the highest fitness for reproduction and obtain new individuals through crossover and mutation;

[0024] Iteration unit, used to repeat the fitness evaluation and breeding mutation process until the iteration stop condition is met and the optimization result is obtained;

[0025] The optimization result output unit is used to output the optimization result.

[0026] In a second aspect, the present invention also provides a control method for the IBC system test and parameter optimization system for multi-road condition simulation, comprising the following steps:

[0027] Step 1: Road condition selection and setting: According to the test objectives, select or establish a road condition simulation model from the road condition database and send it to the brake system test platform;

[0028] Step 2: Vehicle parameter input: input basic vehicle parameters;

[0029] Step 3: Initialize the brake system test platform: load the road condition simulation model, adjust the initial posture of the multi-axis drive test bench, and simulate the physical conditions corresponding to the test road conditions;

[0030] Step 4: The brake system test platform performs tests and obtains test data;

[0031] Step 5: Parameter Optimization The control system optimizes the IBC system control parameters based on the test data obtained from the vehicle braking system, and finally feeds back the parameter optimization results to the braking system test platform for effect verification, adjusts the parameters that do not perform well, and enters the next round of testing;

[0032] Step 6: After multiple rounds of testing and optimization, the optimal IBC control strategy is finally determined.

[0033] Furthermore, the step five comprises:

[0034] S1. Initialization settings: define optimization parameters, and perform parameter encoding and population initialization:

[0035] S11. Define the optimization parameters as: brake pressure, ABS threshold and ESP control gain;

[0036] S12. Assume that the population size is N, the parameter dimension of each individual is M, and the initial population is represented by a matrix:

[0037]

[0038] Among them, P NM represents the Mth parameter of the Nth individual;

[0039] S2. Fitness evaluation:

[0040] The fitness function FF is defined as:

[0041] F(Pi)=-(D(Pi)+T(Pi)-S(Pi))

[0042] Among them, D, T and S are the braking distance, response time and stability function of individual Pi respectively, and the fitness can be expressed as a vector:

[0043]

[0044] The higher the fitness value, the better the individual performance;

[0045] S3. Select several individuals with the highest fitness for reproduction:

[0046] Define the set of selected individuals as SS,

[0047] S = {Pi|F(Pi) is the fitness value of the first N / 2}

[0048] S4. Crossover and mutation:

[0049] The crossover operation generates a new individual C, assuming that Pa and Pb are the selected parent individuals:

[0050] C=1 / 2(Pa+Pb)

[0051] For each pair of parents Pa and Pb, N crossovers are performed to generate N offspring individuals;

[0052] The mutation operation is applied to the new individual Cj, assuming the mutation rate is m:

[0053] Cj=Cj+∈, where ∈~N(0,σ2) is random noise, that is, the parameters are randomly selected for Gaussian noise perturbation;

[0054] S5. Iterative update:

[0055] Update the new population C to the new population P′, and repeat the above steps S3 and S4 until the specified generation GG is reached:

[0056] P′=C

[0057] The best individual finally output is:

[0058]

[0059] The present invention has the following beneficial effects:

[0060] The present invention provides an IBC system test and parameter optimization system for multi-road condition simulation. The parameter optimization system is provided with a road condition model database. Combined with a brake system test platform, a multi-axis drive test bench and a test bench controller can simulate a variety of complex actual road conditions.

[0061] The parameter optimization system adopts a parameter optimization algorithm based on AI algorithm, which can automatically optimize the control parameters of the IBC system according to real-time road condition simulation and vehicle response, so that it can maintain optimal performance under complex road conditions.

[0062] The parameter optimization algorithm based on AI algorithm greatly reduces the need for manual intervention and improves the efficiency and accuracy of IBC system testing, thereby realizing efficient debugging and optimization of the IBC system, which can greatly improve the reliability and controllability of the IBC system under complex driving conditions.

[0063] The parameter optimization algorithm based on AI algorithm has self-learning ability. Through accumulated test data, the system can continuously improve its adaptability to complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the specific implementation of the present invention or the technical solution in the prior art, the drawings required for use in the specific implementation or the description of the prior art are briefly introduced below.

[0065] Figure 1 This is a block diagram showing the composition principle of an IBC system test and parameter optimization system for multi-road condition simulation as described in Example 1 of the present invention;

[0066] Figure 2 This is a schematic diagram of the structure of the multi-axis drive gantry described in Example 1 of the present invention;

[0067] Figure 3 This is a workflow diagram of an IBC system testing and parameter optimization system for multi-road condition simulation described in Example 2 of the present invention. DETAILED DESCRIPTION

[0068] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Example 1

[0070] like Figure 1 As shown, this embodiment is an IBC system test and parameter optimization system for multi-road condition simulation, which consists of a brake system test platform and a parameter optimization control system;

[0071] The brake system test platform is used to simulate the physical conditions corresponding to the test road conditions, conduct vehicle brake system tests, obtain control signals output by the vehicle simulation model, and collect test data of the vehicle brake system;

[0072] The parameter optimization control system is used to send test instructions to the brake system test platform, and optimize the IBC system control parameters according to the test data obtained from the vehicle brake system. Finally, the parameter optimization results are fed back to the brake system test platform for effect verification.

[0073] Furthermore, the brake system test platform comprises:

[0074] Multi-axis drive gantry, such as Figure 2 As shown, it is equipped with the main actuators of the vehicle braking system, including the brake pedal, brake controller, brake caliper and hydraulic circuit; according to the control signal of the test bench controller, it simulates the physical conditions of different road conditions such as different slopes, friction coefficients, bumps, etc., and simulates the execution actions of the vehicle braking system, including the driver's behavior of stepping on the brake pedal;

[0075] The sensor assembly integrates a wheel speed sensor, accelerometer, gyroscope and brake force sensor to collect the physical parameters of the vehicle braking system during the test in real time;

[0076] The test bench controller is used to refer the road condition simulation model to the vehicle simulation model to obtain the control signal output by the vehicle simulation model; send posture control instructions to the multi-axis drive test bench to simulate the physical conditions corresponding to the test road conditions and the execution actions of the vehicle braking system, obtain the physical parameters collected by the sensor assembly and the vehicle status signal output by the vehicle simulation model, and send the test data to the parameter optimization control system.

[0077] Furthermore, the parameter optimization control system comprises:

[0078] A road condition database is used to establish and store road condition simulation models of various typical road conditions. According to the test requirements, the road condition simulation model is selected or established and sent to the bench controller of the brake system test platform; typical road conditions include urban roads, highways, rural roads, etc.;

[0079] A data interaction module, used for performing data interaction with the brake system test platform, sending test control instructions and road condition simulation model data to the brake system test platform, and receiving test data sent by the brake system test platform;

[0080] The IBC system parameter dynamic optimization module optimizes the IBC system parameters through genetic algorithms based on the test data, and feeds the optimization results back to the brake system test platform, repeating multiple rounds of testing and optimization.

[0081] Furthermore, the IBC system parameter dynamic optimization module includes:

[0082] Initialization unit, used to define optimization parameters, and perform parameter encoding and population initialization;

[0083] Fitness evaluation unit, used to evaluate the fitness of IBC under various parameter settings;

[0084] The reproduction and mutation unit is used to select the individuals with the highest fitness for reproduction and obtain new individuals through crossover and mutation;

[0085] Iteration unit, used to repeat the fitness evaluation and breeding mutation process until the iteration stop condition is met and the optimization result is obtained;

[0086] The optimization result output unit is used to output the optimization result.

[0087] Example 2

[0088] This embodiment is a control method for an IBC system test and parameter optimization system for multi-road condition simulation, which is implemented by the system described in Embodiment 1 and includes the following steps:

[0089] Step 1: Road condition selection and setting: According to the test objectives, select or establish a road condition simulation model from the road condition database and send it to the brake system test platform;

[0090] Step 2: Vehicle parameter input: Input the basic parameters of the vehicle, such as wheelbase, mass, tire type, etc., to ensure the authenticity of the test environment;

[0091] Step 3: Initialize the brake system test platform: load the road condition simulation model, adjust the initial posture of the multi-axis drive test bench, and simulate the physical conditions corresponding to the test road conditions;

[0092] Step 4: The brake system test platform performs tests and obtains test data;

[0093] Step 5: Parameter Optimization The control system optimizes the IBC system control parameters based on the test data obtained from the vehicle braking system, and finally feeds back the parameter optimization results to the braking system test platform for effect verification, adjusts the parameters that do not perform well, and enters the next round of testing;

[0094] Step 6: After multiple rounds of testing and optimization, the optimal IBC control strategy is finally determined.

[0095] In this embodiment, the working process of the brake system test platform is as follows:

[0096] Build a virtual test environment under laboratory conditions to simulate the working conditions encountered in real vehicle testing;

[0097] The brake pedal opening under the current test condition is output through the vehicle simulation model and converted into the position simulation motor stroke in the multi-axis drive test bench;

[0098] According to the push rod stroke calculated by the vehicle simulation model, the position simulation motor is position controlled to push the push rod of the ibooster assembly to move the corresponding displacement;

[0099] The internal travel sensor of the Ibooster assembly obtains displacement information and transmits it to the Ibooster controller, which calculates the required deceleration of the vehicle;

[0100] The ibooster controller controls the actuator motor, which is converted into master cylinder pressure together with the push rod force and transmitted to each wheel cylinder;

[0101] The sensor collects wheel cylinder pressure signals and brake caliper pressure signals, and the vehicle simulation model calculates the wheel speed and braking distance.

[0102] In this embodiment, the optimization process of the IBC system parameter dynamic optimization module is as follows:

[0103] S1. Initialization settings: define optimization parameters, and perform parameter encoding and population initialization:

[0104] S11. Define the optimization parameters as follows: brake pressure (0-100 bar); ABS threshold (0-1.0); ESP control gain (0-10);

[0105] S12. Assume that the population size is N, the parameter dimension of each individual is M, and the initial population is represented by a matrix:

[0106]

[0107] Among them, P NM Represents the Mth parameter of the Nth individual.

[0108] S2. Fitness evaluation:

[0109] The fitness function FF is defined as:

[0110] F(Pi)=-(D(Pi)+T(Pi)-S(Pi))

[0111] Among them, D, T and S are the braking distance, response time and stability function of individual Pi respectively, and the fitness can be expressed as a vector:

[0112]

[0113] The higher the fitness value, the better the individual performance.

[0114] S3. Select several individuals with the highest fitness for reproduction:

[0115] Define the set of selected individuals as SS,

[0116] S = {Pi|F(Pi) is the fitness value of the first N / 2}

[0117] This process can be expressed as sorting the FFs and selecting the half with the highest fitness.

[0118] S4. Crossover and mutation:

[0119] The crossover operation generates a new individual C, assuming that Pa and Pb are the selected parent individuals:

[0120] C=1 / 2(Pa+Pb)

[0121] For each pair of parents Pa and Pb, N crossovers are performed to generate N offspring individuals.

[0122] The mutation operation is applied to the new individual Cj, assuming the mutation rate is m:

[0123] Cj=Cj+∈, where ∈~N(0,σ2) is random noise

[0124] That is, randomly select parameters for Gaussian noise perturbation.

[0125] S5. Iterative update:

[0126] Update the new population C to the new population P′, and repeat the above steps S3 and S4 until the specified generation GG is reached:

[0127] P′=C

[0128] The best individual finally output is:

[0129]

[0130] Explanation of terms involved in the present invention:

[0131] IBC: Integrated Brake Control, integrated brake control system

[0132] AI: Artificial Intelligence

[0133] ABS: Anti-lock Braking System

[0134] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0135] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0136] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. An IBC system test and parameter optimization system for multi-road condition simulation, characterized in that: It consists of a brake system test platform and a parameter optimization control system; The brake system test platform is used to simulate the physical conditions corresponding to the test road conditions, conduct vehicle brake system tests, obtain control signals output by the vehicle simulation model, and collect test data of the vehicle brake system; The parameter optimization control system is used to send test instructions to the brake system test platform, and optimize the IBC system control parameters according to the test data obtained from the vehicle brake system. Finally, the parameter optimization results are fed back to the brake system test platform for effect verification.

2. The IBC system testing and parameter optimization system for multi-road condition simulation according to claim 1, characterized in that: The brake system test platform comprises: A multi-axis drive test bench equipped with a brake pedal, brake controller, brake caliper and hydraulic circuit of a vehicle braking system; based on the control signal of the test bench controller, it simulates the physical conditions of different road conditions and the execution action of the vehicle braking system; Sensor assembly, used to collect physical parameters of the vehicle braking system during the test in real time; The test bench controller is used to refer the road condition simulation model to the vehicle simulation model to obtain the control signal output by the vehicle simulation model; send posture control instructions to the multi-axis drive test bench to simulate the physical conditions corresponding to the test road conditions and the execution actions of the vehicle braking system, obtain the physical parameters collected by the sensor assembly and the vehicle status signal output by the vehicle simulation model, and send the test data to the parameter optimization control system.

3. The IBC system testing and parameter optimization system for multi-road condition simulation as claimed in claim 2, characterized in that: The physical conditions for simulating different road conditions include slope, friction coefficient, and bumpiness.

4. The IBC system testing and parameter optimization system for multi-road condition simulation as claimed in claim 2, characterized in that: The sensor assembly includes a wheel speed sensor, an accelerometer, a gyroscope and a braking force sensor.

5. The IBC system testing and parameter optimization system for multi-road condition simulation according to claim 1, characterized in that: The parameter optimization control system comprises: A road condition database, used to establish and store road condition simulation models of various typical road conditions, and select or establish a road condition simulation model according to test requirements and send it to the bench controller of the brake system test platform; A data interaction module, used for performing data interaction with the brake system test platform, sending test control instructions and road condition simulation model data to the brake system test platform, and receiving test data sent by the brake system test platform; The IBC system parameter dynamic optimization module optimizes the IBC system parameters through genetic algorithms based on the test data, and feeds the optimization results back to the brake system test platform, repeating multiple rounds of testing and optimization.

6. The IBC system testing and parameter optimization system for multi-road condition simulation as claimed in claim 5, characterized in that: The typical road conditions include urban roads, expressways, and rural roads.

7. The IBC system testing and parameter optimization system for multi-road condition simulation as claimed in claim 5, characterized in that: The IBC system parameter dynamic optimization module includes: Initialization unit, used to define optimization parameters, and perform parameter encoding and population initialization; Fitness evaluation unit, used to evaluate the fitness of IBC under various parameter settings; The reproduction and mutation unit is used to select the individuals with the highest fitness for reproduction and obtain new individuals through crossover and mutation; Iteration unit, used to repeat the fitness evaluation and breeding mutation process until the iteration stop condition is met and the optimization result is obtained; The optimization result output unit is used to output the optimization result.

8. A control method for an IBC system test and parameter optimization system for multi-road condition simulation as described in any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: Road condition selection and setting: According to the test objectives, select or establish a road condition simulation model from the road condition database and send it to the brake system test platform; Step 2: Vehicle parameter input: input basic vehicle parameters; Step 3: Initialize the brake system test platform: load the road condition simulation model, adjust the initial posture of the multi-axis drive test bench, and simulate the physical conditions corresponding to the test road conditions; Step 4: The brake system test platform performs tests and obtains test data; Step 5: Parameter Optimization The control system optimizes the IBC system control parameters based on the test data obtained from the vehicle braking system, and finally feeds back the parameter optimization results to the braking system test platform for effect verification, adjusts the parameters that do not perform well, and enters the next round of testing; Step 6: After multiple rounds of testing and optimization, the optimal IBC control strategy is finally determined.

9. The control method of the IBC system test and parameter optimization system for multi-road condition simulation as claimed in claim 8, characterized in that: The step five comprises: S1. Initialization settings: define optimization parameters, and perform parameter encoding and population initialization: S11. Define the optimization parameters as: brake pressure, ABS threshold and ESP control gain; S12. Assume that the population size is N, the parameter dimension of each individual is M, and the initial population is represented by a matrix: Among them, P NM represents the Mth parameter of the Nth individual; S2. Fitness evaluation: The fitness function FF is defined as: F(Pi)=-(D(Pi)+T(Pi)-S(Pi)) Among them, D, T and S are the braking distance, response time and stability function of individual Pi respectively, and the fitness can be expressed as a vector: The higher the fitness value, the better the individual performance; S3. Select several individuals with the highest fitness for reproduction: Define the set of selected individuals as SS, S = {Pi|F(Pi) is the fitness value of the first N / 2} S4. Crossover and mutation: The crossover operation generates a new individual C, assuming that Pa and Pb are the selected parent individuals: C=1 / 2(Pa+Pb) For each pair of parents Pa and Pb, N crossovers are performed to generate N offspring individuals; The mutation operation is applied to the new individual Cj, assuming the mutation rate is m: Cj=Cj+∈, where ∈~N(0,σ2) is random noise, that is, the parameters are randomly selected for Gaussian noise perturbation; S5. Iterative update: Update the new population C to the new population P′, and repeat the above steps S3 and S4 until the specified generation GG is reached: P′=C The best individual finally output is:

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