Vehicle braking system parameter optimization method and optimization system

Abstract

The invention relates to a vehicle braking system parameter optimization method and an optimization system thereof. The method comprises the following steps: a setting step: setting a vehicle braking system parameter optimization target, determining a to-be-optimized vehicle braking system parameter, and setting a working condition according to the to-be-optimized vehicle braking parameter; a simulation step of establishing a simulation environment and simulating a set working condition; a calculation step: extracting a vehicle signal related to the braking performance from the simulation result for each group of vehicle braking system parameters, and calculating an evaluation value of the braking performance of each group of vehicle braking system parameters based on the vehicle signal and a target; a judgment step: judging whether the evaluation value of the braking performance reaches a target or not, if not, adjusting parameters of a vehicle braking system and repeating iteration to perform the simulation step and the calculation step until the evaluation value reaches the target, and if yes, performing an output step; and an output step: outputting the vehicle braking system parameters and the corresponding evaluation values. According to the invention, the simulation environment can be used to replace a real vehicle and the simulation efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to vehicle control technology, and in particular to an optimization method and an optimization system for vehicle braking system parameters. Background Art

[0002] The functional parameters of an integrated braking system (IPB) or an electronic stability program (ESP) need to be tuned before software release. In the prior art, the tuning of these functional parameters is achieved by performing on-vehicle tests and measuring, recording, and analyzing the results.

[0003] However, the prior art has the following problems, for example:

[0004] Limitations of vehicle resources and test sites;

[0005] The tuning process depends on the knowledge and experience of application engineers, and there is no objective standard for some working conditions, resulting in low consistency in the execution of working conditions;

[0006] Due to time constraints, it is difficult to balance parameter tuning in multiple performance dimensions. Summary of the Invention

[0007] Based on the problems in the above prior art, the present invention aims to provide an optimization method and an optimization system for vehicle braking system parameters that can use a simulation environment to replace on-vehicle tests.

[0008] Furthermore, the present invention also aims to provide an optimization method and an optimization system for vehicle braking system parameters that can implement parameter tuning using machine algorithms and can achieve an optimized balance of parameters in different performance dimensions.

[0009] The optimization method for vehicle braking system parameters according to one aspect of the present invention includes:

[0010] A setting step of setting the goal of optimizing vehicle braking system parameters, determining the vehicle braking system parameters to be optimized, and setting working conditions according to the vehicle braking parameters to be optimized;

[0011] A simulation step of establishing a simulation environment and simulating the set working conditions;

[0012] A calculation step of extracting vehicle signals related to braking performance from the simulation results for each set of vehicle braking system parameters and calculating an evaluation value of the braking performance of each set of vehicle braking system parameters based on the vehicle signals and the goal;

[0013] A judgment step of judging whether the evaluation value of the braking performance reaches the goal. If not, adjusting the vehicle braking system parameters and repeating the simulation step and the calculation step iteratively until the goal is reached. If so, performing the following output step; and

[0014] An output step for outputting vehicle braking system parameters and corresponding evaluation values.

[0015] An optimization system for vehicle braking system parameters according to one aspect of the present invention, characterized by comprising:

[0016] A setting module for setting the goal of optimizing vehicle braking system parameters and determining the vehicle braking system parameters to be optimized, and setting the working conditions according to the vehicle braking parameters to be optimized;

[0017] A simulation module for establishing a simulation environment and simulating the working conditions;

[0018] A calculation module for extracting vehicle signals related to braking performance from the simulation results for each set of vehicle braking system parameters and calculating the evaluation value of the braking performance of each set of vehicle braking system parameters based on the vehicle signals and the goal;

[0019] A judgment module for judging whether the evaluation value reaches the goal. If not, the vehicle braking system parameters are adjusted and the simulation module and the calculation module are repeatedly iterated until the goal is reached. If so, the following output module is performed; and

[0020] An output module for outputting vehicle braking system parameters and corresponding evaluation values. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] From the following detailed description in conjunction with the accompanying drawings, the above and other objects and advantages of the present application will become more completely clear, wherein the same or similar elements are denoted by the same reference numerals.

[0022] Figure 1 is a schematic flowchart of an optimization method for vehicle braking system parameters according to an embodiment of the present invention.

[0023] Figure 2 is a schematic flowchart of an example of the optimization process.

[0024] Figure 3 is a structural block diagram of an optimization system for vehicle braking system parameters according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The following describes some of the multiple embodiments of the present invention, aiming to provide a basic understanding of the present invention, and not aiming to identify the key or decisive elements of the present invention or to limit the scope to be protected.

[0026] Figure 1 is a schematic flowchart of an optimization method for vehicle braking system parameters according to an embodiment of the present invention.

[0027] As Figure 1As shown in the figure, the optimization method for vehicle braking system parameters according to an embodiment of the present invention includes the following steps:

[0028] Step S1: Start;

[0029] Step S2: Set the goal, define the working conditions, and determine the vehicle braking system parameters to be optimized (hereinafter simply referred to as "parameters"). Among them, the so-called goal refers to the goal to be achieved by optimizing the vehicle braking system parameters. For example, by adjusting the vehicle braking system parameters to increase the deceleration to control a shorter braking distance (for example, less than 50 m). In this case, the goal is a braking distance of less than 50 m. The working condition refers to the simulation road of the vehicle simulation model, and the vehicle braking system parameters are set according to different goals to be optimized (for example, the ABS function), and the simulation road file of the corresponding working condition written by the simulation engineer;

[0030] Step S3: Set the simulation environment according to the input information. The input information includes, for example, software information (the software version should be consistent with the version used in the vehicle) and vehicle information (mass, wheelbase, drag coefficient, etc.) to establish the simulation environment;

[0031] Step S4: Simulate the defined working conditions;

[0032] Step S5: Evaluate the simulation results and adjust the parameters. For example, use an AI-based algorithm to score the simulation results according to the set goal to obtain an evaluation value. Specifically, for each set of parameters, extract vehicle signals related to braking performance from the simulation results generated by the simulation (which are consistent with the sensors on the actual test vehicle), such as deceleration, wheel speed, angular velocity, etc., and calculate the corresponding evaluation value according to the set goal (for example, if the braking distance calculated from the vehicle signal is 40 m, then the difference from the goal of less than 50 m is -10 m). The relevant content will be described with reference to Figure 2 for illustration;

[0033] Step S6: Determine whether the goal is achieved. If not (N), then adjust the parameters and return to Step S2. Otherwise (Y), proceed to Step S7. Among them, the so-called "determine whether the goal is achieved" can include two aspects: on the one hand, determine whether the set maximum number of iterations is reached, and on the other hand, the evaluation value no longer improves after a certain number of iterations (for example, no better parameters appear after 300 iterations after a set of parameters are updated). Meeting either aspect is considered to have reached the iterative goal; and

[0034] Step S7: Rank all the simulation results according to the evaluation value. The relevant content will be described with reference to Figure 2 for illustration.

[0035] Among them, as the vehicle braking system parameters, include but are not limited to the following parameters:

[0036] (1) ABS boosting parameters: front axle first wheel boost amount, rear axle first wheel boost amount, front axle first wheel boost gradient, ABS trigger threshold parameter;

[0037] (2) Front axle slip ratio threshold;

[0038] (3) Rear axle slip ratio threshold.

[0039] Figure 2 It is a process schematic diagram of an example of the optimization process.

[0040] As Figure 2 shown, an example of the parameter optimization process of the present invention includes the following steps:

[0041] Step a1: Obtain simulation results;

[0042] Step a2: Extract vehicle signals related to braking performance from the simulation results;

[0043] Step a3: For each set of parameters, evaluate the vehicle signals in different braking performance dimensions to obtain evaluation values, and the evaluation values are used to find optimized parameters;

[0044] Step a4: Determine whether the iteration target is reached. If not (N), adjust the parameters and return to step a1. Otherwise (Y), continue to step e; and

[0045] Step a5: Output the optimization result. As an example, display the parameter ranking with evaluation values. For example, it can be displayed in the order from high to low evaluation values.

[0046] Among them, extracting vehicle signals related to performance. As an example, in the scenario where the braking distance under the optimized ABS function trigger is taken as the target, the vehicle signals related to this braking performance include deceleration, wheel cylinder pressure, etc. Among them, as an example, in different braking performance dimensions under this target, such as braking distance, braking comfort (which can be calculated by the fluctuation deviation of deceleration, the smaller the deviation, the higher the comfort), etc.

[0047] Among them, evaluating the vehicle signals in different braking performance dimensions for each set of parameters to obtain evaluation values can be achieved through an AI algorithm model. As an example, input the vehicle braking system parameters, vehicle signals related to braking performance, and the set target into the AI algorithm model. Use the AI algorithm model to calculate the evaluation values based on the vehicle signals related to braking performance and the set target, and output each set of parameters and their corresponding evaluation values.

[0048] As an example, the Bayesian model can be used as the AI algorithm model. Using the Bayesian model can improve the efficiency of optimizing parameters. This is because, in order to narrow the search space of the optimization parameters during optimization, in essence, all parameter combinations can be traversed by brute force to find the optimal parameters. However, in practice, the traversal method requires a large amount of time cost, while using the Bayesian module can save time and improve efficiency.

[0049] On the other hand, in the case where there are multiple braking performance dimensions, it is also possible to calculate the comprehensive evaluation value of each group of parameters according to the weights of different braking performance dimensions based on the user's preferences. Here, the user's preferences can be realized by the user defining weights for them, and finally the comprehensive evaluation value is obtained through weighted calculation based on the weights.

[0050] The following lists an example of setting weights for different braking performance dimensions and calculating the comprehensive evaluation value.

[0051] For each group of parameters, a corresponding simulation result will be obtained. Vehicle signals related to braking performance are extracted from the simulation results to calculate the evaluation value of braking performance (i.e., the braking distance, braking comfort, etc. described above), which is denoted as "score" here. Among them, assume there are parameter group 1, parameter group 2, parameter group 3... and assume the evaluation is carried out from performance 1 and performance 2:

[0052] The evaluation value of performance 1 is score1, and the evaluation value of performance 2 is score2

[0053] If user 1 uses weights a1 and a2, the calculation formula for the comprehensive evaluation value is as follows:

[0054] score_a = a1 * score1 + a2 * score2

[0055] Then the score for each group of parameters is:

[0056] Parameter group 1: score_a1

[0057] Parameter group 2: score_a2

[0058] Parameter group 3: score_a3

[0059] ……

[0060] If user 2 uses weights b1 and b2, the calculation formula for the comprehensive evaluation value is as follows:

[0061] score_b = b1 * score1 + b2 * score2

[0062] Then the score for each group of parameters is:

[0063] Parameter group 1: score_b1

[0064] Parameter group 2: score_b2

[0065] Parameter group 3: score_b3

[0066] ……

[0067] By adopting such weights set according to user preferences, it is possible to consider both performance indicators and user preferences.

[0068] The above describes the optimization method for the vehicle braking system parameters of the present invention. Next, the optimization system for the vehicle braking system parameters of an embodiment of the present invention will be described.

[0069] Figure 3 is the structural block diagram of the optimization system for the vehicle braking system parameters of an embodiment of the present invention.

[0070] As Figure 3 shown, the optimization system 100 for the vehicle braking system parameters of an embodiment of the present invention includes:

[0071] A setting module 110, configured to set the goal of optimizing the vehicle braking system parameters and determine the vehicle braking system parameters to be optimized, and set the working conditions according to the vehicle braking parameters to be optimized;

[0072] A simulation module 120, configured to establish a simulation environment and simulate the working conditions;

[0073] A calculation module 130, configured to extract vehicle signals related to braking performance from the simulation results for each group of vehicle braking system parameters and calculate the evaluation value of the braking performance of each group of vehicle braking system parameters based on the vehicle signals and the goal;

[0074] A judgment module 140, configured to judge whether the evaluation value reaches the goal. If not, adjust the vehicle braking system parameters and repeat the iteration of the simulation module and the calculation module until the goal is reached. If so, proceed to the following output module; and an output module 150, configured to output the vehicle braking system parameters and the corresponding evaluation values.

[0075] Among them, in the calculation module 130, a Bayesian model is used to calculate the evaluation value of the braking performance of each group of vehicle braking system parameters based on the vehicle signals and the goal. And, in the calculation module 130, multiple evaluation values of braking performance are calculated respectively according to different braking performance dimensions according to the user's preferences, and the comprehensive evaluation value of different braking performance dimensions is calculated according to the weights of different braking performance dimensions.

[0076] Among them, in the output module 150, the ranking of vehicle braking system parameters with evaluation values is displayed. For example, the display can be performed in the order from high to low evaluation values.

[0077] As described above, the method and system for optimizing vehicle braking system parameters according to the present invention can be applied to the tuning of functional parameters of an integrated braking system (IPB) and an electronic stability control system (ESP).

[0078] Moreover, according to the method and system for optimizing vehicle braking system parameters of the present invention, a simulation environment can be used to replace a real vehicle in a predefined scenario, thereby saving vehicle resources and test site resources and effectively reducing costs.

[0079] Furthermore, by using an AI optimization algorithm in the calculation step, automated measurement evaluation and parameter tuning are performed according to objective criteria extracted from empirically verified measurements and engineer experience, and the process of parameter tuning can be standardized.

[0080] Moreover, by using the fine-tuning parameters of the Bayesian algorithm, user preferences will be considered, so as to obtain an optimized balance result in different performance dimensions.

[0081] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Those skilled in the art can think of other feasible changes or substitutions according to the technical scope disclosed in this application, and such changes or substitutions are all covered by the protection scope of this application. Without conflict, the implementation manners of this application and the features in the implementation manners can also be combined with each other. The protection scope of this application shall be subject to the content recorded in the claims.

Claims

1. An optimization method for vehicle braking system parameters, characterized in that, Comprising: A setting step of setting the objective of optimizing the vehicle braking system parameters and determining the vehicle braking system parameters to be optimized, and setting the working conditions according to the vehicle braking parameters to be optimized; A simulation step of establishing a simulation environment and simulating the set working conditions; A calculation step of extracting vehicle signals related to braking performance from the simulation results for each set of vehicle braking system parameters and calculating the evaluation value of the braking performance of each set of vehicle braking system parameters based on the vehicle signals and the objective; A judgment step of judging whether the evaluation value of the braking performance reaches the objective. If not, adjusting the vehicle braking system parameters and repeating the iteration for the simulation step and the calculation step until the objective is reached. If so, performing the following output step; and An output step of outputting the vehicle braking system parameters and the corresponding evaluation values.

2. The method for optimizing vehicle braking system parameters according to claim 1, wherein in the calculation step, a Bayesian model is used to calculate the evaluation value of the braking performance of each set of vehicle braking system parameters based on the vehicle signals and the objective.

3. The method for optimizing vehicle braking system parameters according to claim 2, wherein in the calculation step, each set of vehicle braking system parameters, the vehicle signals related to braking performance, and the objective are input into the Bayesian model, and the Bayesian model is used to calculate the evaluation value of the braking performance of each set of vehicle braking system parameters based on the vehicle signals related to braking performance and the objective.

4. The method for optimizing vehicle braking system parameters according to claim 3, wherein in the calculation step, calculating the evaluation value of the braking performance of each set of vehicle braking system parameters includes: calculating multiple evaluation values of braking performance respectively in different braking performance dimensions according to the user's preference and calculating the comprehensive evaluation value of different braking performance dimensions according to the weights of different braking performance dimensions.

5. The method for optimizing vehicle braking system parameters according to claim 4, wherein in the calculation step, calculating multiple evaluation values of braking performance respectively in different braking performance dimensions and calculating the comprehensive evaluation value of different braking performance dimensions according to the weights of different braking performance dimensions includes: setting N braking performance dimensions, where N is a natural number; calculating N evaluation values of braking performance respectively for the N braking performance dimensions; and setting weights for the N evaluation values of braking performance respectively and summing the N evaluation values of braking performance based on the weights to obtain the comprehensive evaluation value.

6. The method for optimizing vehicle braking system parameters according to claim 5, wherein judging whether the evaluation value reaches the objective in the judgment step includes any one of the following: judging whether the number of iterations reaches the preset maximum number of iterations; and judging that the evaluation value does not improve after the specified number of iterations.

7. The method for optimizing vehicle braking system parameters according to claim 1, wherein the vehicle braking system parameters include one or more of the following: ABS boost parameter; Front axle slip ratio threshold; and Rear axle slip ratio threshold.

8. An optimization system for vehicle braking system parameters, characterized in that, Comprising: A setting module, configured to set the objectives for optimizing the parameters of a vehicle braking system and determine the vehicle braking system parameters to be optimized, and set the operating conditions according to the vehicle braking parameters to be optimized; A simulation module, configured to establish a simulation environment and simulate the operating conditions; A calculation module, configured to extract vehicle signals related to braking performance from the simulation results for each set of vehicle braking system parameters and calculate an evaluation value of the braking performance of each set of vehicle braking system parameters based on the vehicle signals and the objectives; A judgment module, configured to judge whether the evaluation value reaches the objectives. If not, adjust the vehicle braking system parameters and repeat the iteration of the simulation module and the calculation module until the objectives are reached. If so, proceed to the following output module; And An output module, configured to output the vehicle braking system parameters and the corresponding evaluation values.

9. The optimization system for vehicle braking system parameters according to claim 1, wherein in the calculation module, a Bayesian model is used to calculate an evaluation value of the braking performance of each set of vehicle braking system parameters based on the vehicle signals and the objectives.

10. The optimization system for vehicle braking system parameters according to claim 3, wherein in the calculation module, evaluation values of multiple braking performances are calculated respectively in different braking performance dimensions according to the user's preferences, and a comprehensive evaluation value of different braking performance dimensions is calculated according to the weights of different braking performance dimensions.

11. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the optimization method for vehicle braking system parameters according to any one of claims 1 to 8.

12. A computer device, comprising a storage module, a processor, and a computer program stored on the storage module and executable on the processor, wherein when the processor executes the computer program, it implements the optimization method for vehicle braking system parameters according to any one of claims 1 to 8.

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