CN-120706071-B - Electronic brake intelligent test simulation method and system based on big data analysis

CN120706071BCN 120706071 BCN120706071 BCN 120706071BCN-120706071-B

Abstract

The invention discloses an electronic brake intelligent test simulation method and system based on big data analysis, which belong to the technical field of intelligent test simulation, wherein brake response delay data, steering angle change data and load change data in a history simulation process are acquired, an operation time period corresponding to each test scene is acquired, a time sequence response data set is constructed, a load-brake efficiency ratio and a steering angular velocity of a single operation time period corresponding to a single test scene are calculated, a steering-load coupling efficiency index is calculated based on the load-brake efficiency ratio and the steering angular velocity, a reference score is constructed, a brake performance score and the caliper abrasion degree of the single operation time period corresponding to the single test scene are calculated by combining the steering-load coupling efficiency index, a threshold value is preset, intelligent test simulation analysis is performed, and therefore intelligent scheduling of dynamic monitoring and test processes is achieved, and finally the effects of improving test efficiency, optimizing test resource allocation and enhancing durability and safety of a system are achieved.

Inventors

LV HAOYU
LI QUANTONG
JI YINGJIE
WANG HENG

Assignees

天津英创汇智汽车技术有限公司

Dates

Publication Date: 20260508
Application Date: 20250617

Claims (8)

1. The intelligent test simulation method for the electronic brake based on big data analysis is characterized by comprising the following steps of: Step S1, acquiring brake response delay data, steering angle change data and load change data in a history simulation process, acquiring a running time period corresponding to each test scene, and constructing a time sequence response data set; S2, calculating the load-braking efficiency ratio and the steering angular speed of a single operation time period corresponding to a single test scene; Step S3, calculating a steering-load coupling efficiency index of a single operation time period corresponding to a single test scene based on the load-braking efficiency ratio and the steering angular speed; S4, calculating the abrasion degree of the calipers in a single operation time period corresponding to a single test scene according to the brake performance score, presetting a threshold value, and performing intelligent test simulation analysis; the specific implementation process of the step S3 includes: Based on test scene Load-brake efficiency ratio for the corresponding nth operating time period And steering angular velocity Calculating a test scene The corresponding steering-to-load coupling efficiency index for the nth run time period is calculated as follows: ; Wherein, the Representing a test scenario The steering-to-load coupling efficiency index for the corresponding nth operating period, Indicating the preset attenuation coefficient of the optical fiber, Representing the i-th test scenario, Representing a test scenario The load-brake efficiency ratio of the corresponding nth operating period, Representing a test scenario Steering angular velocity of the corresponding nth operating period; Presetting ideal brake response delay index And minimum delay protection value And constructing a reference score value, which is recorded as , wherein, Max () represents the function taking the maximum value, Representing a test scenario Brake response delay data of the corresponding nth operation time period, and testing the scene Steering-to-load coupling efficiency index for the corresponding nth operating time period And a reference score value Multiplying and calculating to obtain a test scene The brake performance score for the corresponding nth run time period is noted as ; The specific implementation process of the step S4 includes: Based on test scene Brake performance score for the corresponding nth operating time period Calculating a test scene The caliper wear degree of the corresponding nth operating time period is calculated as follows: ; Wherein, the Representing a test scenario Caliper wear for the corresponding nth operating period, Indicating the preset coefficient of wear and tear, Representing a test scenario Load change data for a corresponding nth run time period; acquiring test scenes Caliper wear for all corresponding run time periods and calculate test scenarios The average value of the abrasion degree of the calipers is preset, and if the test scene is If the average value of the abrasion degree of the calipers is smaller than the threshold value of the abrasion degree of the calipers, judging a test scene Is a low-abrasion scene, if the test scene If the average value of the abrasion degree of the calipers is larger than or equal to the threshold value of the abrasion degree of the calipers, judging a test scene And stopping simulation for the high-abrasion scene, maintaining, and selecting the low-abrasion scene for test simulation in the next operation time period.
2. The simulation method for intelligent testing of electronic brake based on big data analysis according to claim 1, wherein the specific implementation process of step S1 comprises: Based on the HIL simulation test platform, acquiring time sequence response data of an EMB system and a steering system in a history simulation process, wherein the time sequence response data comprises brake response delay data, steering angle change data and load change data, constructing a test scene set, and recording as , wherein, The simulation running time of each historical simulation test is uniformly divided into a plurality of running time periods, and the running time period corresponding to each test scene is obtained, wherein one test scene corresponds to at least one running time period; respectively to test scenes The brake response delay data, the steering angle change data and the load change data of the corresponding operation time period are normalized, and a test scene based is constructed Is recorded as a run time period time sequence response data set of (1) , wherein, Representing a test scenario Brake response delay data for the corresponding nth operating period, Representing a test scenario Steering angle change data for the corresponding nth operating period, Representing a test scenario Load change data of the corresponding nth run time period, N representing a test scenario Corresponding running time period total.
3. The simulation method for intelligent testing of electronic brake based on big data analysis according to claim 2, wherein the specific implementation process of step S2 comprises: Based on test scene Brake response delay data for the corresponding nth operating time period And load change data Calculating a test scene The load-brake efficiency ratio for the corresponding nth operating period is calculated as follows: ; Wherein, the Representing a test scenario The load-brake efficiency ratio of the corresponding nth operating period, Representing a preset error term; Based on test scene Steering angle change data for the corresponding nth operating time period Calculating a test scene The steering angular velocity of the corresponding nth operation period is calculated as follows: ; Wherein, the Representing a test scenario The steering angular velocity of the corresponding nth operating period, Representing a test scenario Steering angle change data for the corresponding n-1 th operation period, Representing the time interval between the nth operating period and the n-1 th operating period.
4. An electronic brake intelligent test simulation system based on big data analysis, which executes the electronic brake intelligent test simulation method based on big data analysis according to any one of claims 1-3, wherein the system comprises a data acquisition and collection construction module, an efficiency ratio and angular speed calculation module, an efficiency index and score calculation module and a wear degree calculation and analysis module; The data acquisition and collection construction module is used for acquiring brake response delay data, steering angle change data and load change data in the history simulation process, acquiring a running time period corresponding to each test scene and constructing a time sequence response data set; The efficiency ratio and angular speed calculation module is used for calculating the load-braking efficiency ratio and steering angular speed of a single operation time period corresponding to a single test scene; the efficiency index and score calculation module is used for calculating the steering-load coupling efficiency index of a single operation time period corresponding to a single test scene based on the load-braking efficiency ratio and the steering angular speed; The abrasion degree calculating and analyzing module is used for calculating the abrasion degree of the calipers in a single operation time period corresponding to a single test scene according to the brake performance score, and carrying out intelligent test simulation analysis by presetting a threshold value.
5. The electronic brake intelligent test simulation system based on big data analysis of claim 4, wherein the data acquisition and collection construction module comprises a data acquisition unit and a collection construction unit; The data acquisition unit is used for acquiring time sequence response data of an EMB system and a steering system in a history simulation process based on an HIL simulation test platform, wherein the time sequence response data comprises brake response delay data, steering angle change data and load change data; The set construction unit is used for respectively carrying out normalization processing on the brake response delay data, the steering angle change data and the load change data of the operation time period corresponding to the test scene and constructing a time sequence response data set of the operation time period based on the test scene.
6. The intelligent test simulation system for the electronic brake based on big data analysis of claim 5, wherein the efficiency ratio and angular velocity calculation module comprises an efficiency ratio calculation unit and an angular velocity calculation unit; The efficiency ratio calculating unit is used for calculating the load-brake efficiency ratio of the nth operation time period corresponding to the test scene based on the brake response delay data and the load change data of the nth operation time period corresponding to the test scene; The angular velocity calculating unit is used for calculating the steering angular velocity of the nth operation time period corresponding to the test scene based on the steering angle change data of the nth operation time period corresponding to the test scene.
7. The intelligent test simulation system for the electronic brake based on big data analysis of claim 6, wherein the efficiency index and score calculation module comprises an efficiency index calculation unit and a score calculation unit; The efficiency index calculation unit is used for calculating a steering-load coupling efficiency index of the nth operation time period corresponding to the test scene based on the load-braking efficiency ratio and the steering angular speed of the nth operation time period corresponding to the test scene; The evaluation calculation unit is used for presetting an ideal brake response delay index and a minimum delay protection value, constructing a reference evaluation value, multiplying the steering-load coupling efficiency index of the nth operation time period corresponding to the test scene by the reference evaluation value, and calculating to obtain the brake performance score of the nth operation time period corresponding to the test scene.
8. The electronic brake intelligent test simulation system based on big data analysis of claim 7, wherein the wear degree calculation and analysis module comprises a wear degree calculation unit and an analysis unit; The abrasion degree calculation unit is used for calculating the abrasion degree of the calipers of the nth operation time period corresponding to the test scene based on the brake performance score of the nth operation time period corresponding to the test scene; the analysis unit is used for acquiring the calliper abrasion degree of all operation time periods corresponding to the test scene, calculating the calliper abrasion degree average value of the test scene, presetting a calliper abrasion degree threshold value, judging the test scene as a low abrasion scene if the calliper abrasion degree average value of the test scene is smaller than the calliper abrasion degree threshold value, judging the test scene as a high abrasion scene if the calliper abrasion degree average value of the test scene is larger than or equal to the calliper abrasion degree threshold value, stopping simulation, maintaining, and selecting the low abrasion scene for test simulation when the next operation time period is reached.

Description

Electronic brake intelligent test simulation method and system based on big data analysis Technical Field The invention relates to the technical field of intelligent test simulation, in particular to an electronic brake intelligent test simulation method and system based on big data analysis. Background In recent years, a novel system represented by Electro mechanical brake (EMB, electro-MECHANICAL BRAKE) does not depend on a hydraulic medium any more, and has the advantages of high response speed, high brake precision, compact structure and the like. However, the electronic brake system is influenced by multi-factor coupling such as environmental change, load dynamic fluctuation, steering angle abrupt change and the like in actual operation, and problems such as response lag, unstable control or increased component wear are easily caused. With the development of a hardware-in-loop (HIL) simulation platform, a data acquisition system and a model deduction algorithm, a simulation test gradually tends to high precision, multidimensional degree and automation, evolves towards a big data driven intelligent simulation direction, and potential performance rules are emphasized to be mined from historical working conditions, so that a behavior modeling and predictive maintenance strategy based on data is realized. However, the existing electronic brake simulation test technology focuses on the evaluation of single parameters such as brake response or friction coefficient, and lacks of dynamic analysis and comprehensive index construction of coupling relation between multiple physical quantities (such as load change and steering behavior) in a complex scene. In addition, the judgment of the test result by the existing method generally depends on manually set rules, and the fine granularity change of the system performance under the actual use condition is difficult to dynamically reflect. For example, for critical components such as caliper wear, the traditional evaluation method is mostly based on periodic detection or experience prediction, and can not be combined with operation data to conduct quantitative trend analysis on wear risk, so that simulation results are difficult to timely feed back, and subsequent test strategy selection is guided. Disclosure of Invention The invention aims to provide an electronic brake intelligent test simulation method and system based on big data analysis, which are used for solving the problems in the background technology. In order to solve the technical problems, the invention provides the following technical scheme: The electronic brake intelligent test simulation method based on big data analysis comprises the following steps of S1, obtaining brake response delay data, steering angle change data and load change data in a history simulation process, obtaining operation time periods corresponding to each test scene, constructing a time sequence response data set, S2, calculating load-brake efficiency ratio and steering angular velocity of the single operation time period corresponding to the single test scene, S3, calculating steering-load coupling efficiency index of the single operation time period corresponding to the single test scene based on the load-brake efficiency ratio and the steering angular velocity, constructing a reference score value, calculating a brake performance score in combination with the steering-load coupling efficiency index, S4, calculating the abrasion degree of calipers of the single operation time period corresponding to the single test scene according to the brake performance score, and presetting a threshold value, and performing intelligent test simulation analysis. As a preferable scheme of the electronic brake intelligent test simulation method based on big data analysis, based on an HIL simulation test platform, time sequence response data of an EMB system and a steering system in a history simulation process are obtained, wherein the time sequence response data comprises brake response delay data, steering angle change data and load change data; constructing a test scene set, and recording the test scene set as CJ= { CJ i |i epsilon [1, I ] }, wherein CJ i represents an ith test scene, and I represents the total number of the test scenes; evenly dividing simulation running time of each historical simulation test into a plurality of running time periods, and obtaining the running time period corresponding to each test scene, wherein one test scene corresponds to at least one running time period; The brake response delay data, the steering angle change data and the load change data of the operation time period corresponding to the test scene cj i are respectively normalized, a time sequence response data set of the operation time period based on the test scene cj i is constructed and marked as TSR i＝{(brdi,n,csai,n,lvi,n) |n epsilon [1, N ] }, wherein brd i,n represents the brake response delay data of the nth operation time period correspond