Optimizing method for vehicle brake system parameter and optimization system
A simulation-based optimization method using AI algorithms addresses the limitations of traditional brake parameter adjustment by optimizing vehicle brake system parameters in a virtual environment, enhancing efficiency and consistency.
Patent Information
- Application Number
- JP2024231958
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2023-12-27
- Filing Date
- 2024-12-27
- Publication Date
- 2025-07-09
AI Technical Summary
Existing methods for adjusting vehicle brake system parameters face limitations due to resource constraints, reliance on engineer expertise, lack of objective criteria, and inconsistent execution, making it difficult to optimize parameters effectively.
A simulation-based optimization method and system using a machine algorithm to adjust brake system parameters in a virtual environment, employing AI algorithms like the Bayesian model to evaluate and iteratively refine parameters until target performance is achieved, considering user preferences.
This approach allows for efficient, standardized parameter optimization in vehicle brakes, reducing resource usage and costs while ensuring consistent performance across various conditions.
Smart Images
Figure 2025104341000001_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to vehicle control technology, and more particularly to an optimization method and an optimization system for vehicle brake system parameters.
Background Art
[0002] The functional parameters of an integrated power brake (IPB) or an electronic stability program (ESP) need to be adjusted before software release. The adjustment of these functional parameters in the prior art is carried out by an actual vehicle and achieved through measurement recording and analysis.
[0003] However, the prior art has problems such as the following, for example. There are limitations in vehicle resources and test sites. The adjustment process depends on the knowledge and experience of application engineers, there is no objective criterion for specific working conditions, and the consistency of the execution of working conditions is low. Due to time constraints, it is difficult to weight parameter adjustment according to multiple performance elements.
Summary of the Invention
Problems to be Solved by the Invention
[0004] Based on the above problems in the prior art, an object of the present invention is to provide an optimization method and an optimization system for vehicle brake system parameters, which can replace an actual vehicle using a simulation environment.
[0005] Furthermore, an object of the present invention is to provide an optimization method and an optimization system for vehicle brake system parameters, which can realize parameter adjustment using a machine algorithm and obtain a parameter optimization balance among different performance elements.
Means for Solving the Problems
[0006] In one aspect of the present invention, a method for optimizing vehicle brake system parameters includes: a setting step of setting an optimization target for the vehicle brake system parameters, determining the vehicle brake system parameters to be optimized, and setting operating conditions according to the vehicle brake system parameters to be optimized; a simulation step of establishing a simulation environment and simulating the set operating conditions; for each set of vehicle brake system parameters, extracting vehicle signals related to brake performance from the simulation results, and calculating an evaluation value of the brake performance of each set of vehicle brake system parameters based on the vehicle signals and the target; a determination step of determining whether the evaluation value of the brake performance reaches the target. If not, adjusting the vehicle brake system parameters and repeating the simulation step and the calculation step until the target is reached. If so, executing the following output step; an output step of outputting the vehicle brake system parameters and the corresponding evaluation values; and includes.
[0007] In one aspect of the present invention, an optimization system for vehicle brake system parameters is provided. The optimization system includes: a setting module for setting an optimization target for the vehicle brake system parameters, determining the vehicle brake system parameters to be optimized, and setting operating conditions according to the vehicle brake system parameters to be optimized; a simulation module for establishing a simulation environment and used to simulate the operating conditions; For each set of vehicle brake system parameters, extract vehicle signals related to braking performance from the simulation results, and based on the vehicle signals and the target, a calculation module used to calculate an evaluation value of the braking performance of each set of vehicle brake system parameters. A determination module used to determine whether the evaluation value has reached the target. If it has not reached, adjust the vehicle brake system parameters, and repeat the simulation by the simulation module and the calculation by the calculation module until the target is reached. If it has reached, execute the output by the following output module. An output module used to output the vehicle brake system parameters and the corresponding evaluation values. Comprising.
Brief Description of the Drawings
[0008] The foregoing objects, other objects, and advantages of the present application will become more fully apparent from the following detailed description in conjunction with the accompanying drawings, where identical or similar elements are denoted by the same reference numerals.
Figure 1
Figure 2
Figure 3
Modes for Carrying Out the Invention
[0009] The following describes a part of the embodiments of the present disclosure for the purpose of providing a basic understanding of the present disclosure. It is not intended to identify important elements or decisive elements of the present invention, nor to limit the scope of protection required.
[0010] FIG. 1 is a flowchart of a method for optimizing vehicle brake system parameters according to an embodiment of the present invention.
[0011] As shown in FIG. 1, the method for optimizing vehicle brake system parameters according to an embodiment of the present invention includes the following steps. Step S1: Start; Step S2: A step of setting a target, defining operating conditions, and determining vehicle brake system parameters to be optimized (hereinafter referred to as parameters), where the target indicates a target to be achieved by optimizing the vehicle brake system parameters. For example, when adjusting the vehicle brake system parameters to increase the deceleration and control to achieve a short braking distance (e.g., less than 50 m), the target is a braking distance of less than 50 m. The operating conditions indicate the simulation road of the vehicle simulation model, and the vehicle brake system parameters are set according to different targets (e.g., ABS function) that require optimization. The simulation road file is written by a simulation engineer for the corresponding operating conditions. Step S3: A step of setting a simulation environment according to input information, where the input information includes, for example, software information for establishing the simulation environment (the software version should be consistent with the version used in the vehicle) and vehicle information (mass, wheelbase, wind resistance coefficient, etc.). Step S4: A step of simulating the specified working conditions; Step S5: A step of evaluating the simulation results and performing parameter adjustment. For example, using an algorithm based on AI to score the simulation results based on the set target to obtain an evaluation value. Specifically, for each set of parameters, extracting vehicle signals such as deceleration, vehicle speed, and angular velocity associated with braking performance from the simulation results generated by simulation (matching the sensors on the actual test vehicle), and calculating the corresponding evaluation value according to the set target (for example, if the braking distance is calculated to be 40m by the vehicle signal, and thus the difference from the target of less than 50m is -10m). This step (where the corresponding content is described with reference to FIG. 2); Step S6: A step of determining whether the target has been reached. If not reached (N), adjust the parameters and return to Step S2. Otherwise (Y), proceed to Step S7. The "determining whether the target has been reached" can include two modes. In one mode, it is determined whether the set maximum number of iterations has been reached. In the other mode, it is determined that the evaluation value does not increase after a specific number of iterations (for example, after updating a set of parameters, no better parameters are displayed after 300 iterations). If any of the above modes is satisfied, it is considered that the iteration target has been met. Step S7: A step of ranking all the simulation results based on the evaluation value (where the corresponding content is described with reference to FIG. 2).
[0012] The vehicle brake system parameters include, but are not limited to, the following parameters. (1) ABS pressurization parameters: front axle first wheel pressurization amount, rear axle first wheel pressurization amount, front axle first wheel pressurization gradient, and ABS trigger gate parameters; (2) Front axle slip speed gate; (3) Rear axle slip speed gate.
[0013] FIG. 2 is a flowchart of an example of the optimization process.
[0014] As shown in FIG. 2, an example of the parameter optimization process of the present invention includes the following steps: Step a1: A step of obtaining simulation results; Step a2: A step of extracting vehicle signals related to braking performance from the simulation results; Step a3: For each set of parameters, a step of evaluating the vehicle signal according to different braking performance characteristics to obtain an evaluation value, wherein the evaluation value is used to find the optimized parameters; Step a4: A step of determining whether the iterative goal has been reached. If not reached (N), adjust the parameters and return to step a1. Otherwise (Y), continue with the execution of step e; and, Step a5: A step of outputting the optimization result. As an example, a step of displaying the parameter rank having the evaluation value, for example, in a ranking format having the evaluation values from high to low, may be adopted.
[0015] Vehicle signals related to performance include, for example, deceleration, wheel cylinder pressure, etc. in a scenario where the braking distance functions as a target in the optimization of the ABS function trigger. As an example, different braking performance characteristics based on this target include, for example, braking distance, and braking comfort (which can be calculated by the variation deviation of the deceleration, and the smaller the deviation, the higher the comfort), etc.
[0016] For each set of parameters, the vehicle signal is evaluated with different braking performance characteristics, and an evaluation value that can be achieved by the AI algorithm model is obtained. As an example, vehicle brake system parameters, vehicle signals related to braking performance, and the set target are input into the AI algorithm model, and the evaluation value is calculated using the AI algorithm model based on the vehicle signal related to braking performance and the set target, and each set of parameters and their evaluation values are output.
[0017] As an example, the AI algorithm model can be achieved by adopting a Bayesian model. Using the Bayesian model can improve the parameter optimization efficiency. Because essentially, the optimal parameters can be found by vigorously traversing all combinations of parameters to reduce the search space for optimizing parameters during optimization. However, in reality, the traversal method is very time-consuming. On the other hand, by adopting the Bayesian model, time can be saved and efficiency can be improved.
[0018] In other aspects, in the presence of multiple braking performance characteristics, it is also possible to calculate a comprehensive evaluation value for each set of parameters according to the user's preference by using the weights of different braking performance characteristics. Here, the user's preference can be achieved by the user specifying the weights for the preferences and finally obtaining the comprehensive evaluation value by weighting based on the weights.
[0019] Examples of setting the weights of different braking performance characteristics and calculating the comprehensive evaluation value are listed below.
[0020] For each set of parameters, obtain one corresponding simulation result, extract vehicle signals related to braking performance from the simulation result, and calculate an evaluation value (recorded as a score in this specification) of the braking performance (i.e., the above-mentioned braking distance, braking comfort, etc.). Assume there are parameter set 1, parameter set 2, parameter set 3..., and the evaluation is performed from performance 1 and performance 2.
[0021] The evaluation value of performance 1 is score 1, and the evaluation value of performance 2 is score 2.
[0022] When user 1 uses weights a1 and a2, the formula for calculating the comprehensive evaluation value is as follows. score_a = a1*score1 + a2*score2 The scores for each set of parameters are as follows. Parameter set 1: score_a1 Parameter set 2: score_a2 Parameter set 3: score_a3 …… When user 2 uses weights b1 and b2, the formula for calculating the comprehensive evaluation value is as follows. score_b = b1*score1 + b2*score2 The scores for each set of parameters are as follows. Parameter set 1: score_b1 Parameter set 2: score_b2 Parameter set 3: score_b3 ……
[0023] By adopting such weights set according to the user's preference, it is possible to consider the performance indicator while taking into account the user's preference.
[0024] The method for optimizing vehicle brake system parameters according to the present invention has been described above, and then, an explanation of an optimization system for vehicle brake system parameters according to an embodiment of the present invention will follow.
[0025] FIG. 3 is a structural block diagram of an optimization system for vehicle brake system parameters according to an embodiment of the present invention.
[0026] As shown in FIG. 3, an optimization system 100 for vehicle brake system parameters according to an embodiment of the present invention includes a setting module 110 used to set the optimization target of the vehicle brake system parameters, which determines the vehicle brake system parameters to be optimized and sets operating conditions according to the vehicle brake system parameters to be optimized; a simulation module 120 used to establish a simulation environment and simulate the operating conditions; for each set of vehicle brake system parameters, a calculation module 130 used to extract vehicle signals related to brake performance from simulation results and calculate an evaluation value of the brake performance of each set of vehicle brake system parameters based on the vehicle signals and the target; a determination module 140 used to determine whether the evaluation value reaches the target. If it does not reach, the vehicle brake system parameters are adjusted, and the simulation by the simulation module and the calculation by the calculation module are repeatedly iterated until the target is reached. If it reaches, the output by the following output module is executed; an output module 150 used to output the vehicle brake system parameters and the corresponding evaluation values. The calculation module 130 includes The evaluation value of the braking performance for each set of vehicle brake system parameters is calculated using a Bayesian model based on the vehicle signal and the target. Further, in the calculation module 130, the evaluation values of a plurality of braking performances are respectively calculated according to the preferences of the user in different braking performance characteristics, and the comprehensive evaluation value of different braking performance characteristics is calculated according to the weights of different braking performance characteristics.
[0027] In the output module 150, the ranking of the vehicle brake system parameters having the evaluation value is displayed. For example, a ranking format having the evaluation values from high to low may be adopted for display.
[0028] As described above, the optimization method and optimization system for vehicle brake system parameters according to the present invention can be applied to the adjustment of the functional parameters of the integrated power brake (IPB) and the electronic stability program (ESP).
[0029] Furthermore, the optimization method and system for vehicle brake system parameters according to the present invention can replace the actual vehicle with a simulation environment in a predetermined scenario, thereby saving vehicle resources and test site resources and effectively reducing costs.
[0030] Furthermore, an AI optimization algorithm is used in the calculation process, the automatic measurement evaluation and parameter adjustment are carried out according to the measured values confirmed by experience and the objective criteria extracted from the experience of engineers, and the process of parameter adjustment can be standardized.
[0031] Furthermore, by adopting the fine-tuning parameters of the Bayesian algorithm, the preferences of the user are considered, and as a result, the balance in different performance dimensions is optimized.
[0032] The above description is only for specific embodiments of this application, and the protection scope of this application is not limited thereto. Those skilled in the art can also contemplate other possible modifications or substitutions based on the technical scope disclosed in this application, and all such modifications or substitutions are within the protection scope of this application. If there is no contradiction, the embodiments of this application and the features within the embodiments can also be combined with each other. The protection scope of this application shall follow the protection scope of the claims.
Claims
1. An optimization method for vehicle brake system parameters, comprising: The optimization method includes: A setting step of setting an optimization target for the vehicle brake system parameters, determining the vehicle brake system parameters to be optimized, and setting operating conditions according to the vehicle brake system parameters to be optimized; A simulation step of establishing a simulation environment and simulating the set operating conditions; For each set of the vehicle brake system parameters, extracting a vehicle signal related to brake performance from the simulation results, and calculating an evaluation value of the brake performance of each set of the vehicle brake system parameters based on the vehicle signal and the target; A determination step of determining whether the evaluation value of the brake performance reaches the target. If not, adjusting the vehicle brake system parameters, and repeating the simulation step and the calculation step until the target is reached. If so, executing the following output step; An output step of outputting the vehicle brake system parameters and the corresponding evaluation values; An optimization method comprising the above steps.
2. In the calculation step, the evaluation value of the brake performance of each set of the vehicle brake system parameters is calculated using a Bayesian model based on the vehicle signal and the target. The optimization method for vehicle brake system parameters according to Claim 1.
3. In the calculation step, each set of the vehicle brake system parameters, the vehicle signal related to the brake performance, and the target are input into the Bayesian model, and the evaluation value of the brake performance of each set of the vehicle brake system parameters is calculated using the Bayesian model based on the vehicle signal related to the brake performance and the target. The optimization method for vehicle brake system parameters according to Claim 2.
4. The calculation step of calculating the evaluation value of the brake performance of each set of the vehicle brake system parameters is as follows: Calculating evaluation values of a plurality of brake performances according to user preferences in different brake performance characteristics respectively, and calculating a comprehensive evaluation value of the different brake performance characteristics according to weights of the different brake performance characteristics, The optimization method for vehicle brake system parameters according to claim 3.
5. In the calculating step, the step of calculating the evaluation values of the plurality of brake performances of the different brake performance characteristics respectively, and calculating the comprehensive evaluation value of the different brake performance characteristics according to the weights of the different brake performance characteristics is A step of setting N brake performance characteristics, where N is a natural number, For the N brake performance characteristics, a step of calculating evaluation values of N brake performances respectively, Setting weights of the evaluation values of the N brake performances respectively, and obtaining the comprehensive evaluation value by summing up the evaluation values of the N brake performances based on the weights, including The optimization method for vehicle brake system parameters according to claim 4.
6. The determination step of determining whether the evaluation value of the brake performance reaches the target is A step of determining whether the number of iterations reaches a preset maximum number of iterations, A step of determining that the evaluation value does not increase after a specified number of iterations including any of The optimization method for vehicle brake system parameters according to claim 5.
7. The vehicle brake system parameters are ABS pressurization parameters, Front axle slip speed gates, and Rear axle slip speed gates including one or more of The optimization method for vehicle brake system parameters according to claim 1.
8. An optimization system for vehicle brake system parameters, wherein the optimization system includes A setting module for setting an optimization target of the vehicle brake system parameters, which determines the vehicle brake system parameters to be optimized and sets operating conditions according to the vehicle brake system parameters to be optimized, A simulation module for establishing a simulation environment and using the set operating conditions for simulation, For each set of the vehicle brake system parameters, a vehicle signal related to the braking performance is extracted from the simulation results, and a calculation module used to calculate an evaluation value of the braking performance of each set of the vehicle brake system parameters based on the vehicle signal and the target; A determination module used to determine whether the evaluation value has reached the target, and if not, adjust the vehicle brake system parameters, and repeat the simulation by the simulation module and the calculation by the calculation module until the target is reached, and if it has reached, execute the output by the following output module; An output module used to output the vehicle brake system parameters and the corresponding evaluation values; An optimization system comprising the above.
9. In the calculation module, the evaluation value of the braking performance of each set of the vehicle brake system parameters is calculated using a Bayesian model based on the vehicle signal and the target. An optimization system for the vehicle brake system parameters according to claim 8.
10. In the calculation module, evaluation values of a plurality of braking performances are respectively calculated according to the preferences of the user in different braking performance characteristics, and a comprehensive evaluation value of the different braking performance characteristics is calculated according to the weights of the different braking performance characteristics. An optimization system for the vehicle brake system parameters according to claim 9.
11. A computer-readable medium storing a computer program for implementing the optimization method for the vehicle brake system parameters according to any one of claims 1 to 7 when executed by a processor.
12. A storage module; A processor; A computer program stored on the storage module and executable on the processor; In a computer device comprising the above; A computer device in which the processor implements the optimization method for the vehicle brake system parameters according to any one of claims 1 to 7 when executing the computer program.