An electronic brake intelligent test simulation method and system based on big data analysis

Abstract

The application discloses an electronic brake intelligent test simulation method and system based on big data analysis, and belongs to the technical field of intelligent test simulation; brake response delay data, steering angle change data and load change data in a historical simulation process are acquired; a running time period corresponding to each test scene is acquired, and a time sequence response data set is constructed; load-brake efficiency ratio and steering angular velocity of a single running time period corresponding to a single test scene are calculated; based on the load-brake efficiency ratio and the steering angular velocity, steering-load coupling efficiency index is calculated; a benchmark score value is constructed, brake performance score and caliper wear degree of a single running time period corresponding to a single test scene are calculated in combination with the steering-load coupling efficiency index; a preset threshold value is used for intelligent test simulation analysis, so that dynamic monitoring and intelligent scheduling of a test process are realized, and the effects of improving test efficiency, optimizing test resource configuration and enhancing system durability and safety are finally achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent testing and simulation technology, specifically to an intelligent testing and simulation method and system for electronic braking based on big data analysis. Background Technology

[0002] In recent years, new systems represented by electro-mechanical braking (EMB) have emerged, no longer relying on hydraulic fluids and possessing advantages such as fast response speed, high braking accuracy, and compact structure. However, in actual operation, electronic braking systems are susceptible to problems such as response lag, control instability, or accelerated component wear due to the coupling effects of multiple factors, including environmental changes, dynamic load fluctuations, and sudden changes in steering angle. With the development of hardware-in-the-loop (HIL) simulation platforms, data acquisition systems, and model inference algorithms, simulation testing is gradually trending towards higher precision, multi-dimensionality, and automation, and is evolving towards big data-driven intelligent simulation, emphasizing the extraction of potential performance patterns from historical operating conditions to achieve data-based behavioral modeling and predictive maintenance strategies.

[0003] However, existing electronic braking simulation testing technologies mostly focus on evaluating single parameters such as braking response time or friction coefficient, lacking dynamic analysis and comprehensive index construction of the coupling relationships between multiple physical quantities (such as load changes and steering behavior) in complex scenarios. Furthermore, existing methods typically rely on manually set rules to evaluate test results, making it difficult to dynamically reflect fine-grained changes in system performance under actual usage conditions. For example, for critical components such as caliper wear, traditional evaluation methods are mostly based on periodic inspections or empirical predictions, failing to combine operational data to quantify wear risk trends and providing timely feedback on simulation results to guide subsequent testing strategy selection. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent testing and simulation method and system for electronic braking based on big data analysis, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A smart test simulation method for electronic braking based on big data analysis includes the following steps: Step S1: Obtain braking response delay data, steering angle change data, and load change data from historical simulation processes; obtain the running time period corresponding to each test scenario and construct a time-series response dataset; Step S2: Calculate the load-braking efficiency ratio and steering angular velocity for a single running time period corresponding to a single test scenario; Step S3: Calculate the steering-load coupling efficiency index for a single running time period corresponding to a single test scenario based on the load-braking efficiency ratio and steering angular velocity; construct a benchmark score and calculate the braking performance score by combining the steering-load coupling efficiency index; Step S4: Calculate the caliper wear degree for a single running time period corresponding to a single test scenario based on the braking performance score; preset a threshold and perform smart test simulation analysis.

[0007] As a preferred embodiment of the electronic braking intelligent test simulation method based on big data analysis described in this invention, the method uses the HIL simulation test platform to acquire the timing response data of the EMB system and steering system during historical simulations. This timing response data includes braking response delay data, steering angle change data, and load change data. A test scenario set is then constructed, denoted as CJ = {cj}. i |i∈[1,I]}, where cj i Let i represent the i-th test scenario, and I represent the total number of test scenarios. Divide the simulation runtime of each historical simulation test evenly into several runtime periods, and obtain the runtime corresponding to each test scenario. Each test scenario corresponds to at least one runtime period.

[0008] For the test scenario cj i The braking response delay data, steering angle change data, and load change data for the corresponding operating time period are normalized, and a test scenario-based cj is constructed. i The time-series response dataset for the runtime period is denoted as TSR. i ={(brd i,n ,csa i,n ,lv i,n )|n∈[1,N]}, where brd i,n The test scenario is represented by cj. i The corresponding braking response delay data for the nth running time period, csa i,n The test scenario is represented by cj. i The corresponding steering angle change data for the nth running time period, lv i,n The test scenario is represented by cj. i The load change data for the corresponding nth runtime period, where N represents the test scenario cj. i The total number of corresponding running time periods.

[0009] As a preferred embodiment of the electronic braking intelligent test simulation method based on big data analysis described in this invention, based on the test scenario cj i Braking response delay data brd for the corresponding nth running time period i,n and load change data lv i,n Calculate the test scenario cj i The load-braking efficiency ratio for the corresponding nth operating time period is calculated using the following formula:

[0010]

[0011] Among them, eff i,n The test scenario is represented by cj. i The load-braking efficiency ratio for the corresponding nth operating time period, where ∈ represents a preset error term;

[0012] It should be noted that, from a physical perspective and considering the scenario of this electronic braking intelligent test simulation method, the load change data (lv) i,n Divide by braking response delay data i,n This method can obtain the load change rate under unit braking response delay, which reflects the relative rate of load change in the time dimension of braking response delay. It is used to analyze the impact of braking response delay on load change and to help evaluate the characteristics of electronic braking system in response to load change under different response delays. For example, it can determine the efficiency of load change and system adaptability under different braking delays. It is a calculation and analysis method that extracts data value from the perspective of the correlation between the two.

[0013] Based on test scenario cj i The corresponding steering angle change data for the nth running time period (csa) i,n Calculate the test scenario cj i The formula for calculating the steering angular velocity for the corresponding nth running time period is as follows:

[0014]

[0015] Among them, acc i,n The test scenario is represented by cj. i The corresponding turning angular velocity, csa, for the nth running time period i,n-1 The test scenario is represented by cj. i The corresponding turning angle change data for the (n-1)th running time period, where Δt represents the time interval between the nth and (n-1)th running time periods.

[0016] As a preferred embodiment of the electronic braking intelligent test simulation method based on big data analysis described in this invention, based on the test scenario cj iThe load-braking efficiency ratio eff corresponding to the nth operating time period i,n and steering angular velocity acc i,n Calculate the test scenario cj i The steering-load coupling efficiency index for the corresponding nth running time period is calculated using the following formula:

[0017]

[0018] Among them, STE i,n The test scenario is represented by cj. i The steering-load coupling efficiency index for the nth running time period, where γ represents the preset attenuation coefficient;

[0019] It should be noted that this formula uses the load-braking efficiency ratio eff i,n With steering angular velocity acc i,n Coupled through the exponential decay function, the exponential term This represents the suppressive effect of steering angular velocity on coupling efficiency, where γ is the attenuation coefficient that adjusts the weight of the steering effect; the more abrupt the steering, the lower the coupling efficiency. i,n The larger the value of |, the smaller the value of the exponent and the lower the coupling efficiency exponent, reflecting the mutual interference between steering and load changes. This formula comprehensively evaluates the coupling effect between steering and load changes, reflecting the coordination ability of the electronic braking system under the interaction of multiple physical quantities.

[0020] Preset ideal braking response delay index λ ideal and minimum delay protection value λ min And construct a benchmark score, denoted as S. base ,in, `max()` represents the function to retrieve the maximum value; the test scenario `cj` will be used. i The corresponding STE (Switch-Load Coupling Efficiency Index) for the nth running time period. i,n Compared with the benchmark score S base Multiply by the product to calculate the test scenario cj. i The braking performance score for the corresponding nth operating time period is denoted as BPS. i,n .

[0021] As a preferred embodiment of the electronic braking intelligent test simulation method based on big data analysis described in this invention, based on the test scenario cj i Braking performance score (BPS) for the corresponding nth operating time period i,n Calculate the test scenario cj i The caliper wear for the corresponding nth operating time period is calculated using the following formula:

[0022] CW i,n =μ×(1-BPS) i,n)×lv i,n ;

[0023] Among them, CW i,n The test scenario is represented by cj. i The caliper wear degree corresponding to the nth running time period, where μ represents the preset wear coefficient;

[0024] It should be noted that this formula utilizes the Braking Performance Score (BPS). i,n The complement (1-BPS) i,n This indicates the degree of performance defect, multiplied by the load variation data (lv). i,n The wear coefficient μ is used to calculate the wear degree of the caliper. The worse the performance, the higher the wear degree. The formula is linearly related to the performance score and the amount of wear. The greater the load and the worse the performance, the higher the wear degree. It can quantify the wear risk of key components (calipers) and provide data support for system maintenance.

[0025] Get test scene cj i The wear of the calipers during the entire runtime period was calculated, and the test scenario cj was also calculated. i The average caliper wear value, the preset caliper wear threshold, if the test scenario is cj i If the average wear value of the calipers is less than the caliper wear threshold, then the test scenario is determined to be cj. i For low-wear scenarios; if the test scenario is cj i If the average wear value of the calipers is greater than or equal to the caliper wear threshold, then the test scenario is determined to be cj. i If the scenario is a high-wear scenario, the simulation will be stopped for maintenance, and a low-wear scenario will be selected for testing and simulation in the next running period.

[0026] An intelligent testing and simulation system for electronic braking based on big data analysis. The system includes: a data acquisition and set construction module, an efficiency ratio and angular velocity calculation module, an efficiency index and score calculation module, and a wear degree calculation and analysis module.

[0027] The data acquisition and dataset construction module acquires braking response delay data, steering angle change data, and load change data from historical simulation processes; it also acquires the running time period corresponding to each test scenario and constructs a time-series response dataset.

[0028] The efficiency ratio and angular velocity calculation module calculates the load-braking efficiency ratio and steering angular velocity for a single operating time period corresponding to a single test scenario.

[0029] The efficiency index and scoring calculation module calculates the steering-load coupling efficiency index for a single test scenario and a single operating time period based on the load-brake efficiency ratio and steering angular velocity; it constructs a benchmark score and calculates the braking performance score by combining the steering-load coupling efficiency index.

[0030] The wear calculation and analysis module calculates the caliper wear for a single operating time period corresponding to a single test scenario based on the braking performance score; and performs intelligent test simulation analysis based on a preset threshold.

[0031] Furthermore, the data acquisition and collection construction module includes a data acquisition unit and a collection construction unit;

[0032] The data acquisition unit: Based on the HIL simulation test platform, acquires the timing response data of the EMB system and steering system during historical simulations. The timing response data includes braking response delay data, steering angle change data, and load change data. Construct a set of test scenarios. Divide the simulation run time of each historical simulation test evenly into several run time periods and acquire the run time period corresponding to each test scenario. Each test scenario corresponds to at least one run time period.

[0033] The set construction unit performs normalization processing on the braking response delay data, steering angle change data, and load change data of the running time period corresponding to the test scenario, and constructs a time-series response dataset based on the running time period of the test scenario.

[0034] Furthermore, the efficiency ratio and angular velocity calculation module includes an efficiency ratio calculation unit and an angular velocity calculation unit;

[0035] The efficiency ratio calculation unit calculates the load-braking efficiency ratio for the nth operating time period corresponding to the test scenario based on the braking response delay data and load change data for the nth operating time period corresponding to the test scenario.

[0036] The angular velocity calculation unit calculates the angular velocity of the nth running time period corresponding to the test scenario based on the turning angle change data of the nth running time period corresponding to the test scenario.

[0037] Furthermore, the efficiency index and score calculation module includes an efficiency index calculation unit and a score calculation unit;

[0038] The efficiency index calculation unit calculates the steering-load coupling efficiency index for the nth operating time period corresponding to the test scenario, based on the load-braking efficiency ratio and steering angular velocity for the nth operating time period corresponding to the test scenario.

[0039] The scoring calculation unit: presets an ideal braking response delay index and a minimum delay protection value, and constructs a benchmark score value; multiplies the steering-load coupling efficiency index of the nth operating time period corresponding to the test scenario with the benchmark score value to calculate the braking performance score of the nth operating time period corresponding to the test scenario.

[0040] Furthermore, the wear calculation and analysis module includes a wear calculation unit and an analysis unit;

[0041] The wear calculation unit calculates the caliper wear during the nth running time period corresponding to the test scenario based on the braking performance score of the nth running time period corresponding to the test scenario.

[0042] The analysis unit acquires the caliper wear for all running time periods corresponding to the test scenario, calculates the average caliper wear for the test scenario, and presets a caliper wear threshold. If the average caliper wear for the test scenario is less than the caliper wear threshold, the test scenario is determined to be a low-wear scenario; if the average caliper wear for the test scenario is greater than or equal to the caliper wear threshold, the test scenario is determined to be a high-wear scenario, the simulation is stopped, maintenance is performed, and a low-wear scenario is selected for test simulation in the next running time period.

[0043] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention provides an intelligent electronic braking test simulation method and system based on big data analysis. By acquiring historical time-series data such as braking response delay, steering angle change, and load change based on the HIL simulation platform, a standardized response dataset at the runtime level is constructed, enabling a fine depiction of dynamic behavior in the test scenario and laying the foundation for subsequent multi-dimensional analysis. Furthermore, by calculating the load-braking efficiency ratio and steering angular velocity, the dynamic relationship between braking response and load change, as well as steering change characteristics, are extracted, achieving quantitative analysis of the performance and adaptability of the electronic braking system under different operating states. Subsequently, a steering-load coupling efficiency index is constructed based on these two indicators, and a benchmark scoring mechanism is introduced to achieve a comprehensive evaluation of the system's coupling characteristics and coordination capabilities, thereby improving the intelligent analysis depth and decision-making ability of the simulation test. Finally, by estimating caliper wear based on the braking performance score and combining it with preset thresholds for scenario determination, dynamic monitoring of the status of key components and intelligent scheduling of the test process are achieved, ultimately resulting in improved test efficiency, optimized test resource allocation, and enhanced system durability and safety. Attached Figure Description

[0044] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0045] Figure 1 This is a schematic diagram illustrating the steps of an intelligent testing and simulation method for electronic braking based on big data analysis according to the present invention.

[0046] Figure 2 This is a schematic diagram of the structure of an intelligent test and simulation system for electronic braking based on big data analysis according to the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Please see Figure 1 In this first embodiment: an intelligent testing and simulation method for electronic braking based on big data analysis is provided, which includes the following steps:

[0049] Step S1: Obtain braking response delay data, steering angle change data, and load change data from historical simulation processes; obtain the running time period corresponding to each test scenario and construct a time-series response dataset.

[0050] Specifically, based on the HIL simulation test platform, timing response data of the EMB system and steering system during historical simulations are acquired. This timing response data includes braking response delay data, steering angle change data, and load change data. A test scenario set is constructed, denoted as CJ = {cj}. i |i∈[1,I]}, where cj i Let i represent the i-th test scenario, and I represent the total number of test scenarios. Divide the simulation runtime of each historical simulation test evenly into several runtime periods, and obtain the runtime corresponding to each test scenario. Each test scenario corresponds to at least one runtime period.

[0051] Furthermore, the test scenarios cj were analyzed separately. i The braking response delay data, steering angle change data, and load change data for the corresponding operating time period are normalized, and a test scenario-based cj is constructed. i The time-series response dataset for the runtime period is denoted as TSR. i ={(brd i,n ,csa i,n ,lv i,n )|n∈[1,N]}, where brd i,n The test scenario is represented by cj. i The corresponding braking response delay data for the nth running time period, csa i,n The test scenario is represented by cj. i The corresponding steering angle change data for the nth running time period, lv i,n The test scenario is represented by cj. i The load change data for the corresponding nth runtime period, where N represents the test scenario cj.i The total number of corresponding running time periods.

[0052] Step S2: Calculate the load-braking efficiency ratio and steering angular velocity for a single running time period corresponding to a single test scenario.

[0053] Specifically, based on the test scenario cj i Braking response delay data brd for the corresponding nth running time period i,n and load change data lv i,n Calculate the test scenario cj i The load-braking efficiency ratio for the corresponding nth operating time period is calculated using the following formula:

[0054]

[0055] Among them, eff i,n The test scenario is represented by cj. i The load-braking efficiency ratio for the corresponding nth operating time period, where ∈ represents a preset error term;

[0056] In this invention, the load handling capacity per unit braking delay time is quantified by the ratio of load change to braking delay, directly reflecting the load adaptability of the electronic braking system under response lag. For example, under heavy load conditions, a short braking delay and a high load change rate result in a large efficiency ratio, indicating strong system response capability. Calculating the efficiency ratio using historical data allows for the establishment of a load-braking delay correlation model based on big data analysis, providing a quantitative basis for system optimization. For instance, comparing the efficiency ratio distribution of different vehicle models can identify design flaws in the braking system.

[0057] Furthermore, based on the test scenario cj i The corresponding steering angle change data for the nth running time period (csa) i,n Calculate the test scenario cj i The formula for calculating the steering angular velocity for the corresponding nth running time period is as follows:

[0058]

[0059] Among them, acc i,n The test scenario is represented by cj. i The corresponding turning angular velocity, csa, for the nth running time period i,n-1 The test scenario is represented by cj. i The corresponding turning angle change data for the (n-1)th running time period, where Δt represents the time interval between the nth and (n-1)th running time periods.

[0060] Step S3: Based on the load-braking efficiency ratio and steering angular velocity, calculate the steering-load coupling efficiency index for a single operating time period corresponding to a single test scenario; construct a benchmark score, and calculate the braking performance score by combining the steering-load coupling efficiency index.

[0061] Specifically, based on the test scenario cj i The load-braking efficiency ratio eff corresponding to the nth operating time period i,n and steering angular velocity acc i,n Calculate the test scenario cj i The steering-load coupling efficiency index for the corresponding nth running time period is calculated using the following formula:

[0062]

[0063] Among them, STE i,n The test scenario is represented by cj. i The steering-load coupling efficiency index for the nth running time period, where γ represents the preset attenuation coefficient;

[0064] In this invention, an exponential decay function is used to characterize the suppressive effect of steering on load-braking efficiency: the more abrupt the steering (|acc)... i,n The larger the value of the coupling efficiency index, the more significant the decrease. This aligns with actual physical laws—during sharp turns, the vehicle's center of gravity shifts, significantly affecting braking performance (e.g., increased load on one wheel leads to uneven braking force). By combining load handling efficiency with steering dynamics, a comprehensive quantification of the "braking-steering" coupling condition can be achieved. For example, in a fully loaded sharp turn scenario, this index can simultaneously reflect the combined effects of load change rate and steering disturbance, identifying the system's performance critical point.

[0065] Furthermore, the ideal braking response delay index λ is preset. ideal and minimum delay protection value λ min And construct a benchmark score, denoted as S. base ,in, `max()` represents the function to retrieve the maximum value; the test scenario `cj` will be used. i The corresponding STE (Switch-Load Coupling Efficiency Index) for the nth running time period. i,n Compared with the benchmark score S base Multiply by the product to calculate the test scenario cj. i The braking performance score for the corresponding nth operating time period is denoted as BPS. i,n .

[0066] In this invention, the braking performance score multiplies the coupling efficiency index (reflecting actual operating conditions) by the benchmark score (reflecting ideal performance), forming a composite scoring system of "actual performance * ideal standard". For example, if a scenario has high coupling efficiency but long braking delay, the score will be lowered due to the low benchmark score, prompting the system to prioritize the optimization of key performance bottlenecks.

[0067] Step S4: Based on the braking performance score, calculate the caliper wear degree for a single operating time period corresponding to a single test scenario; preset a threshold and perform intelligent test simulation analysis.

[0068] Specifically, based on the test scenario cj i Braking performance score (BPS) for the corresponding nth operating time period i,n Calculate the test scenario cj i The caliper wear for the corresponding nth operating time period is calculated using the following formula:

[0069] CW i,n =μ×(1-BPS) i,n )×lv i,n ;

[0070] Among them, CW i,n The test scenario is represented by cj. i The caliper wear degree corresponding to the nth running time period, where μ represents the preset wear coefficient;

[0071] Furthermore, obtain the test scenario cj i The wear of the calipers during the entire runtime period was calculated, and the test scenario cj was also calculated. i The average caliper wear value, the preset caliper wear threshold, if the test scenario is cj i If the average wear value of the calipers is less than the caliper wear threshold, then the test scenario is determined to be cj. i For low-wear scenarios; if the test scenario is cj i If the average wear value of the calipers is greater than or equal to the caliper wear threshold, then the test scenario is determined to be cj. i If the scenario is a high-wear scenario, the simulation will be stopped for maintenance, and a low-wear scenario will be selected for testing and simulation in the next running period.

[0072] Please see Figure 2 In this second embodiment: an intelligent test simulation system for electronic braking based on big data analysis is provided. The system includes: a data acquisition and set construction module, an efficiency ratio and angular velocity calculation module, an efficiency index and score calculation module, and a wear degree calculation and analysis module.

[0073] The data acquisition and dataset construction module acquires braking response delay data, steering angle change data, and load change data from historical simulation processes; it also acquires the running time period corresponding to each test scenario and constructs a time-series response dataset.

[0074] The efficiency ratio and angular velocity calculation module calculates the load-braking efficiency ratio and steering angular velocity for a single operating time period corresponding to a single test scenario.

[0075] The efficiency index and scoring calculation module calculates the steering-load coupling efficiency index for a single test scenario and a single operating time period based on the load-brake efficiency ratio and steering angular velocity; it constructs a benchmark score and calculates the braking performance score by combining the steering-load coupling efficiency index.

[0076] The wear calculation and analysis module calculates the caliper wear for a single operating time period corresponding to a single test scenario based on the braking performance score; and performs intelligent test simulation analysis based on a preset threshold.

[0077] Furthermore, the data acquisition and collection construction module includes a data acquisition unit and a collection construction unit;

[0078] The data acquisition unit: Based on the HIL simulation test platform, acquires the timing response data of the EMB system and steering system during historical simulations. The timing response data includes braking response delay data, steering angle change data, and load change data. Construct a set of test scenarios. Divide the simulation run time of each historical simulation test evenly into several run time periods and acquire the run time period corresponding to each test scenario. Each test scenario corresponds to at least one run time period.

[0079] The set construction unit performs normalization processing on the braking response delay data, steering angle change data, and load change data of the running time period corresponding to the test scenario, and constructs a time-series response dataset based on the running time period of the test scenario.

[0080] Furthermore, the efficiency ratio and angular velocity calculation module includes an efficiency ratio calculation unit and an angular velocity calculation unit;

[0081] The efficiency ratio calculation unit calculates the load-braking efficiency ratio for the nth operating time period corresponding to the test scenario based on the braking response delay data and load change data for the nth operating time period corresponding to the test scenario.

[0082] The angular velocity calculation unit calculates the angular velocity of the nth running time period corresponding to the test scenario based on the turning angle change data of the nth running time period corresponding to the test scenario.

[0083] Furthermore, the efficiency index and score calculation module includes an efficiency index calculation unit and a score calculation unit;

[0084] The efficiency index calculation unit calculates the steering-load coupling efficiency index for the nth operating time period corresponding to the test scenario, based on the load-braking efficiency ratio and steering angular velocity for the nth operating time period corresponding to the test scenario.

[0085] The scoring calculation unit: presets an ideal braking response delay index and a minimum delay protection value, and constructs a benchmark score value; multiplies the steering-load coupling efficiency index of the nth operating time period corresponding to the test scenario with the benchmark score value to calculate the braking performance score of the nth operating time period corresponding to the test scenario.

[0086] Furthermore, the wear calculation and analysis module includes a wear calculation unit and an analysis unit;

[0087] The wear calculation unit calculates the caliper wear during the nth running time period corresponding to the test scenario based on the braking performance score of the nth running time period corresponding to the test scenario.

[0088] The analysis unit acquires the caliper wear for all running time periods corresponding to the test scenario, calculates the average caliper wear for the test scenario, and presets a caliper wear threshold. If the average caliper wear for the test scenario is less than the caliper wear threshold, the test scenario is determined to be a low-wear scenario; if the average caliper wear for the test scenario is greater than or equal to the caliper wear threshold, the test scenario is determined to be a high-wear scenario, the simulation is stopped, maintenance is performed, and a low-wear scenario is selected for test simulation in the next running time period.

[0089] Please refer to Table 1. In this third embodiment, an intelligent testing simulation method for electronic braking based on big data analysis is provided. To verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0090] Test scenario: The preset test scenario is 1 (I=1), with a total of 3 running time periods (N=3), a time interval Δt of 1 second, an error term ∈ of 0.1, an attenuation coefficient γ of 0.1, and an ideal braking response delay index λ. ideal The minimum delay protection value is λ, which is 0.3 seconds. min The wear time is 0.2 seconds, the wear coefficient μ is 1.0, and the caliper wear threshold is 0.2.

[0091] Table 1 Test Data Table

[0092] Time period Braking response delay Load changes Steering angle change Previous steering angle 1 0.6 0.8 0.7 0.5 2 0.5 0.7 0.8 0.7 3 0.4 0.6 0.9 0.8

[0093] Based on Table 1, the calculation is as follows: eff 1,2=1.1667, eff 1,3 =1.2, acc 1,1 =0.2, acc 1,2 =0.1, acc 1,3 =0.1;

[0094] STE 1,1 =1.1429×e -0.1×|0.2| =1.1202, S base =0.5, BPS 1,1 =0.5601;

[0095] STE 1,2 =1.1550, S base =0.6, BPS 1,2 =0.6930;

[0096] STE 1,3 =1.1880, S base =0.75, BPS 1,3 =0.8910;

[0097] CW 1,1 =1.0×(1-0.5601)×0.8=0.3519, CW 1,2 =0.2149, CW 1,3 =0.0654;

[0098] If the average caliper wear value is 0.2107, which is greater than or equal to 0.2, then test scenario cj1 is determined to be a high wear scenario. Therefore, the simulation is stopped, maintenance is performed, and a low wear scenario is selected for test simulation in the next running time period.

[0099] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0100] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart testing and simulation method for electronic braking based on big data analysis, characterized in that, The method includes the following steps: Step S1: Obtain braking response delay data, steering angle change data, and load change data from historical simulations; obtain the running time period corresponding to each test scenario and construct a time-series response dataset; Step S2: Calculate the load-braking efficiency ratio and steering angular velocity for a single running time period corresponding to a single test scenario; Step S3: Based on the load-braking efficiency ratio and steering angular velocity, calculate the steering-load coupling efficiency index for a single operating time period corresponding to a single test scenario; construct a benchmark score, and calculate the braking performance score by combining the steering-load coupling efficiency index; Step S4: Based on the braking performance score, calculate the caliper wear degree for a single operating time period corresponding to a single test scenario; preset a threshold and perform intelligent test simulation analysis; The specific implementation process of step S3 includes: Based on the test scenario The load-braking efficiency ratio for the corresponding nth operating time period and steering angular velocity Calculate the test scenario The steering-load coupling efficiency index for the corresponding nth running time period is calculated using the following formula: ； in, Indicates the test scenario The corresponding turnaround-load coupling efficiency index for the nth running time period. This represents the preset attenuation coefficient. This represents the i-th test scenario. Indicates the test scenario The load-braking efficiency ratio corresponding to the nth operating time period. Indicates the test scenario The turning angular velocity corresponding to the nth running time period; Preset ideal braking response delay index and minimum delay protection value And construct a benchmark score, denoted as ,in, max() represents the function to find the maximum value. Indicates the test scenario The braking response delay data for the corresponding nth running time period; the test scenario The corresponding steering-load coupling efficiency index for the nth running time period Compared with the benchmark score Multiply to calculate the test scenario The braking performance score for the corresponding nth operating time period is denoted as . ; The specific implementation process of step S4 includes: Based on the test scenario Braking performance score for the corresponding nth operating time period Calculate the test scenario The caliper wear for the corresponding nth operating time period is calculated using the following formula: ； in, Indicates the test scenario The caliper wear corresponding to the nth operating time period, This indicates the preset wear coefficient. Indicates the test scenario The load change data for the corresponding nth running time period; Get test scenarios The wear of the calipers was measured for the entire operating time period, and the test scenario was calculated. The average caliper wear value, the preset caliper wear threshold, and the test scenario. If the average wear value of the calipers is less than the caliper wear threshold, then the test scenario is determined. For low-wear scenarios; if the test scenario If the average wear value of the calipers is greater than or equal to the caliper wear threshold, then the test scenario is determined. If the scenario is a high-wear scenario, the simulation will be stopped for maintenance, and a low-wear scenario will be selected for testing and simulation in the next running period.

2. The intelligent testing and simulation method for electronic braking based on big data analysis according to claim 1, characterized in that, The specific implementation process of step S1 includes: Based on the HIL simulation testing platform, timing response data of the EMB system and steering system during historical simulations were acquired. This timing response data includes braking response delay data, steering angle change data, and load change data. A set of test scenarios was constructed, denoted as […]. ,in, Let i represent the i-th test scenario, and I represent the total number of test scenarios. Divide the simulation runtime of each historical simulation test evenly into several runtime periods, and obtain the runtime corresponding to each test scenario. Each test scenario corresponds to at least one runtime period. For each test scenario The braking response delay data, steering angle change data, and load change data for the corresponding operating time period are normalized, and a system based on the test scenario is constructed. The time-series response dataset for the runtime period is denoted as... ,in, Indicates the test scenario The corresponding braking response delay data for the nth running time period. Indicates the test scenario The corresponding steering angle change data for the nth running time period. Indicates the test scenario The load change data for the corresponding nth running time period, where N represents the test scenario. The total number of corresponding running time periods.

3. The intelligent testing and simulation method for electronic braking based on big data analysis according to claim 2, characterized in that, The specific implementation process of step S2 includes: Based on the test scenario Braking response delay data for the corresponding nth running time period and load change data Calculate the test scenario The load-braking efficiency ratio for the corresponding nth operating time period is calculated using the following formula: ； in, Indicates the test scenario The load-braking efficiency ratio corresponding to the nth operating time period. Indicates the preset error term; Based on the test scenario The corresponding steering angle change data for the nth running time period Calculate the test scenario The formula for calculating the steering angular velocity for the corresponding nth running time period is as follows: ； in, Indicates the test scenario The corresponding turning angular velocity during the nth running time period, Indicates the test scenario The corresponding steering angle change data for the (n-1)th running time period. This represents the time interval between the nth running time period and the (n-1)th running time period.

4. An intelligent test simulation system for electronic braking based on big data analysis, executing the intelligent test simulation method for electronic braking based on big data analysis as described in any one of claims 1-3, characterized in that, The system includes: a data acquisition and collection construction module, an efficiency ratio and angular velocity calculation module, an efficiency index and score calculation module, and a wear degree calculation and analysis module; The data acquisition and dataset construction module acquires braking response delay data, steering angle change data, and load change data from historical simulation processes; it also acquires the running time period corresponding to each test scenario and constructs a time-series response dataset. The efficiency ratio and angular velocity calculation module calculates the load-braking efficiency ratio and steering angular velocity for a single operating time period corresponding to a single test scenario. The efficiency index and scoring calculation module calculates the steering-load coupling efficiency index for a single test scenario and a single operating time period based on the load-brake efficiency ratio and steering angular velocity; it constructs a benchmark score and calculates the braking performance score by combining the steering-load coupling efficiency index. The wear calculation and analysis module calculates the caliper wear for a single operating time period corresponding to a single test scenario based on the braking performance score; and performs intelligent test simulation analysis based on a preset threshold.

5. The intelligent test simulation system for electronic braking based on big data analysis according to claim 4, characterized in that: The data acquisition and collection construction module includes a data acquisition unit and a collection construction unit; The data acquisition unit: Based on the HIL simulation test platform, acquires the timing response data of the EMB system and steering system during historical simulations. The timing response data includes braking response delay data, steering angle change data, and load change data. Construct a set of test scenarios. Divide the simulation run time of each historical simulation test evenly into several run time periods and acquire the run time period corresponding to each test scenario. Each test scenario corresponds to at least one run time period. The set construction unit performs normalization processing on the braking response delay data, steering angle change data, and load change data of the running time period corresponding to the test scenario, and constructs a time-series response dataset based on the running time period of the test scenario.

6. The intelligent test simulation system for electronic braking based on big data analysis according to claim 5, characterized in that: The efficiency ratio and angular velocity calculation module includes an efficiency ratio calculation unit and an angular velocity calculation unit; The efficiency ratio calculation unit calculates the load-braking efficiency ratio for the nth operating time period corresponding to the test scenario based on the braking response delay data and load change data for the nth operating time period corresponding to the test scenario. The angular velocity calculation unit calculates the angular velocity of the nth running time period corresponding to the test scenario based on the turning angle change data of the nth running time period corresponding to the test scenario.

7. The intelligent test simulation system for electronic braking based on big data analysis according to claim 6, characterized in that: The efficiency index and score calculation module includes an efficiency index calculation unit and a score calculation unit; The efficiency index calculation unit calculates the steering-load coupling efficiency index for the nth operating time period corresponding to the test scenario, based on the load-braking efficiency ratio and steering angular velocity for the nth operating time period corresponding to the test scenario. The scoring calculation unit: presets an ideal braking response delay index and a minimum delay protection value, and constructs a benchmark scoring value; The braking performance score for the nth operating time period corresponding to the test scenario is calculated by multiplying the steering-load coupling efficiency index of the nth operating time period corresponding to the test scenario by the benchmark score.

8. The intelligent test simulation system for electronic braking based on big data analysis according to claim 7, characterized in that: The wear calculation and analysis module includes a wear calculation unit and an analysis unit; The wear calculation unit calculates the caliper wear during the nth running time period corresponding to the test scenario based on the braking performance score of the nth running time period corresponding to the test scenario. The analysis unit: obtains the caliper wear degree for all running time periods corresponding to the test scenario, calculates the average caliper wear degree of the test scenario, presets a caliper wear degree threshold, and if the average caliper wear degree of the test scenario is less than the caliper wear degree threshold, the test scenario is determined to be a low wear scenario. If the average wear of the calipers in the test scenario is greater than or equal to the caliper wear threshold, the test scenario is determined to be a high-wear scenario. The simulation is then stopped, maintenance is performed, and a low-wear scenario is selected for test simulation in the next running time period.

Images