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

Abstract

The invention 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 the historical simulation process are obtained; obtaining a running time period corresponding to each test scene, and constructing a time sequence response data set; calculating a load-brake efficiency ratio and a steering angular velocity of a single operation time period corresponding to a single test scene; calculating a steering-load coupling efficiency index based on the load-braking efficiency ratio and the steering angular velocity; constructing a reference score value, and calculating a braking performance score and a caliper wear degree of a single operation time period corresponding to a single test scene in combination with the steering-load coupling efficiency index; and a threshold value is preset, and intelligent test simulation analysis is carried out, so that intelligent scheduling of the dynamic monitoring and test process is realized, and finally the effects of improving the test efficiency, optimizing the test resource configuration and enhancing the durability and safety of the system are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent testing and simulation, and in particular to an electronic braking intelligent testing and simulation method and system based on big data analysis. Background Art

[0002] In recent years, new systems, such as electro-mechanical brakes (EMBs), no longer rely on hydraulic media and offer advantages such as fast response, high braking precision, and compact structure. However, in actual operation, electronic brake systems are affected by multiple factors, such as environmental changes, dynamic load fluctuations, and sudden changes in steering angles, which can easily lead to problems such as delayed response, unstable control, and increased component wear. With the development of hardware-in-the-loop (HIL) simulation platforms, data acquisition systems, and model deduction algorithms, simulation testing has gradually become more precise, multi-dimensional, and automated, and is evolving towards big data-driven intelligent simulation, emphasizing the exploration of potential performance patterns from historical operating conditions to implement data-based behavioral modeling and predictive maintenance strategies.

[0003] However, existing electronic brake simulation test technologies mostly focus on the evaluation of single parameters such as brake response time or friction coefficient, and lack the dynamic analysis and comprehensive indicator construction of the coupling relationship between multiple physical quantities (such as load changes and steering behavior) in complex scenarios. In addition, the evaluation of test results by existing methods usually relies on manually set rules, which makes it difficult to dynamically reflect the fine-grained changes in system performance under actual use conditions. For example, for key components such as caliper wear, traditional evaluation methods are mostly based on periodic detection or empirical prediction. They cannot combine operating data to conduct quantitative trend analysis of wear risks, and it is difficult to provide timely feedback on simulation results to guide the subsequent selection of test strategies. Summary of the Invention

[0004] The purpose of the present invention is to provide an electronic brake intelligent test simulation method and system based on big data analysis to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] An electronic brake intelligent test simulation method based on big data analysis, the method comprising the following steps: step S1: obtaining brake response delay data, steering angle change data and load change data in a historical simulation process; obtaining the operating time period corresponding to each test scenario, and constructing a time series response data set; step S2: calculating the load-brake efficiency ratio and steering angular velocity of a single operating time period corresponding to a single test scenario; step S3: calculating the steering-load coupling efficiency index of a single operating time period corresponding to a single test scenario based on the load-brake efficiency ratio and the steering angular velocity; constructing a benchmark score value, and calculating a braking performance score in combination with the steering-load coupling efficiency index; step S4: calculating the caliper wear of a single operating time period corresponding to a single test scenario based on the braking performance score; presetting a threshold value, and performing intelligent test simulation analysis.

[0007] As a preferred solution of the electronic brake intelligent test simulation method based on big data analysis described in the present invention, based on the HIL simulation test platform, the timing response data of the EMB system and the steering system in the historical simulation process are obtained, and the timing response data includes brake 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 represents the i-th test scenario, and I represents the total number of test scenarios; the simulation running time of each historical simulation test is evenly divided into several running time periods, and the running time period corresponding to each test scenario is obtained, wherein one test scenario corresponds to at least one running time period;

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

[0009] As a preferred solution of the electronic brake intelligent test simulation method based on big data analysis described in the present invention, based on the test scenario cj i The corresponding braking response delay data brd of the nth operating time period i,n and load change data lv i,n , calculate the test scenario cj i The corresponding load-brake efficiency ratio for the nth operating time period is calculated as follows:

[0010]

[0011] Among them, eff i,n Represents the test scenario cj i The corresponding load-brake efficiency ratio of the nth operating time period, ∈ represents the preset error term;

[0012] It should be noted that, from the perspective of physical meaning and the scenario of the electronic brake intelligent test simulation method, the load change data lv i,n Divide by the brake response delay data bre i,n , the load change rate under unit braking response delay can be obtained, which can reflect 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 assist in evaluating the characteristics of the electronic braking system in responding to load changes under different response delays. For example, it can judge the efficiency of load change, system adaptability and other aspects when the braking delay is different. It is a computational analysis method to mine the value of data from the perspective of the relationship between the two.

[0013] Based on the test scenario cj i The corresponding steering angle change data csa for the nth operating time period i,n , calculate the test scenario cj i The corresponding steering angular velocity in the nth operating time period is calculated as follows:

[0014]

[0015] Among them, acc i,n Represents the test scenario cj i The corresponding steering angular velocity of the nth operating time period, csa i,n-1 Represents the test scenario cj i The steering angle change data corresponding to the n-1th operating time period, Δt represents the time interval between the n-1th operating time period.

[0016] As a preferred solution of the electronic brake intelligent test simulation method based on big data analysis described in the present invention, based on the test scenario cj iThe corresponding load-brake efficiency ratio eff of 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 of the corresponding n-th operating time period is calculated as follows:

[0017]

[0018] Among them, STE i,n Represents the test scenario cj i The steering-load coupling efficiency index corresponding to the nth operating time period, γ represents the preset attenuation coefficient;

[0019] It should be noted that this formula converts the load-brake efficiency ratio eff i,n and the steering angular velocity acc i,n Through the exponential decay function coupling, the exponential term represents the inhibitory effect of steering angular velocity on coupling efficiency, γ is the attenuation coefficient, which adjusts the weight of steering influence; the more intense the steering, i.e. |acc i,n The larger the |, the smaller the exponential term and the lower the coupling efficiency index, reflecting the mutual interference between steering and load changes. This formula comprehensively evaluates the coupling effect of 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 the minimum delay protection value λ min , and construct a benchmark score value, recorded as S base ,in, max() represents the maximum value function; the test scenario cj i The steering-load coupling efficiency index STE of the corresponding nth operating time period i,n Compared with the benchmark score S base Multiply and calculate the test scenario cj i The corresponding braking performance score of the nth operating time period is recorded as BPS i,n .

[0021] As a preferred solution of the electronic brake intelligent test simulation method based on big data analysis described in the present invention, based on the test scenario cj i The corresponding braking performance score BPS of the nth operating time period i,n , calculate the test scenario cj i The calculation formula for the caliper wear corresponding to the nth operating time period is as follows:

[0022] CW i,n =μ×(1-BPS i,n)×lv i,n ;

[0023] Among them, CW i,n Represents the test scenario cj i The corresponding caliper wear degree of the nth operating time period, μ represents the preset wear coefficient;

[0024] It should be noted that this formula uses the braking performance score BPS i,n The complement of (1-BPS i,n ) represents the degree of performance defect, multiplied by the load change data lv i,n The degree of caliper wear is calculated based on the wear coefficient μ. The worse the performance, the higher the wear. This formula linearly correlates the performance score with the amount of wear. The greater the load and the worse the performance, the higher the wear. The wear risk of key components (calipers) can be quantified to provide data support for system maintenance.

[0025] Get the test scene cj i The corresponding caliper wear of all operating time periods and the calculation of the test scenario cj i The average caliper wear value is set, and the caliper wear threshold is preset. If the test scenario cj i The average caliper wear value of the caliper is less than the caliper wear threshold, then the test scenario cj is determined to be i For low wear scenarios; if the test scenario cj i The average caliper wear value of the caliper is greater than or equal to the caliper wear threshold, then the test scenario cj is determined to be i If it is a high-wear scenario, the simulation is stopped for maintenance, and in the next running time period, a low-wear scenario is selected for test simulation.

[0026] An electronic brake intelligent test simulation system based on big data analysis, 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 calculation and analysis module;

[0027] The data acquisition and collection construction module is used to obtain the braking response delay data, steering angle change data and load change data during the historical simulation process; obtain the operating time period corresponding to each test scenario, and construct a time series response data set;

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

[0029] The efficiency index and score calculation module calculates the steering-load coupling efficiency index for a single operating time period corresponding to a single test scenario based on the load-brake efficiency ratio and the steering angular velocity; constructs a baseline score value, and calculates the braking performance score in combination with the steering-load coupling efficiency index;

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

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

[0032] The data acquisition unit: based on the HIL simulation test platform, acquires the timing response data of the EMB system and the steering system during the historical simulation process, wherein the timing response data includes brake response delay data, steering angle change data, and load change data; constructs a test scenario set; evenly divides the simulation running time of each historical simulation test into a number of running time periods, and acquires the running time period corresponding to each test scenario, wherein one test scenario corresponds to at least one running time period;

[0033] The set construction unit normalizes the braking response delay data, steering angle change data and load change data of the operating time period corresponding to the test scenario, and constructs a time series response data set of the operating time period based on 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-brake efficiency ratio of the nth operating time period corresponding to the test scenario based on the brake response delay data and the load change data of the nth operating time period corresponding to the test scenario;

[0036] The angular velocity calculation unit calculates the steering angular velocity of the nth running time period corresponding to the test scenario based on the steering 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 of the nth operating time period corresponding to the test scenario based on the load-brake efficiency ratio and the steering angular velocity of 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 scoring value; multiplies the steering-load coupling efficiency index of the nth operating time period corresponding to the test scenario by the benchmark scoring value, and calculates 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 of 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: obtains the caliper wear of all operating time periods corresponding to the test scenario, calculates the average caliper wear of the test scenario, and presets a caliper wear threshold. If the average caliper wear of 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 of the test scenario is greater than or equal to the caliper wear threshold, the test scenario is determined to be a high-wear scenario, then the simulation is stopped, maintenance is performed, and in the next operating time period, the low-wear scenario is selected for test simulation.

[0043] Compared with the prior art, the present invention achieves the following beneficial effects: In the electronic brake intelligent test simulation method and system based on big data analysis, the present invention obtains historical time series data such as brake response delay, steering angle change, and load change based on the HIL simulation platform, constructs a standardized response data set at the operating time period level, and achieves a detailed characterization of the dynamic behavior in the test scenario, laying the foundation for subsequent multidimensional analysis. Furthermore, by calculating the load-brake efficiency ratio and steering angular velocity, the dynamic relationship between the brake response and load change and the steering change characteristics are extracted, thereby achieving a quantitative analysis of the performance and adaptability of the electronic brake system under different operating conditions. Subsequently, based on these two indicators, a steering-load coupling efficiency index is constructed, and a benchmark scoring mechanism is introduced to achieve a comprehensive evaluation of the system coupling characteristics and coordination capabilities, thereby enhancing the intelligent analysis depth and decision-making ability of the simulation test. Finally, the caliper wear is estimated based on the braking performance score and combined with preset thresholds for scenario judgment, realizing dynamic monitoring of the key component status and intelligent scheduling of the test process, ultimately achieving the beneficial effects of improving test efficiency, optimizing test resource allocation, and enhancing system durability and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0045] Figure 1 This is a schematic diagram of the steps of an electronic brake intelligent test simulation method based on big data analysis of the present invention;

[0046] Figure 2 It is a structural schematic diagram of an electronic braking intelligent test simulation system based on big data analysis of the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] See also Figure 1 In the first embodiment of the present invention, an electronic brake intelligent test simulation method based on big data analysis is provided, the method comprising the following steps:

[0049] Step S1: Obtain the braking response delay data, steering angle change data, and load change data during the historical simulation process; obtain the operating time period corresponding to each test scenario and construct a time series response data set.

[0050] Specifically, based on the HIL simulation test platform, the time sequence response data of the EMB system and the steering system in the historical simulation process are obtained, and the time sequence response data includes the braking response delay data, the steering angle change data and the load change data; a test scenario set is constructed, which is recorded as CJ = {cj i |i∈[1,I]}, where cj i represents the i-th test scenario, and I represents the total number of test scenarios; the simulation running time of each historical simulation test is evenly divided into several running time periods, and the running time period corresponding to each test scenario is obtained, wherein one test scenario corresponds to at least one running time period;

[0051] Furthermore, the test scene cj i The braking response delay data, steering angle change data and load change data of the corresponding operating time period are normalized, and a test scenario cj is constructed. i The time series response data set of the running time period is recorded as TSR i ={(brd i,n ,csa i,n ,lv i,n )|n∈[1,N]}, where brd i,n Represents the test scenario cj i The corresponding braking response delay data of the nth operating time period, csa i,n Represents the test scenario cj i The corresponding steering angle change data of the nth running time period, lv i,n Represents the test scenario cj i The corresponding load change data of the nth running time period, N represents the test scenario cji The total number of corresponding running time periods.

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

[0053] Specifically, based on the test scenario cj i The corresponding braking response delay data brd of the nth operating time period i,n and load change data lv i,n , calculate the test scenario cj i The corresponding load-brake efficiency ratio for the nth operating time period is calculated as follows:

[0054]

[0055] Among them, eff i,n Represents the test scenario cj i The corresponding load-brake efficiency ratio of the nth operating time period, ∈ represents the preset error term;

[0056] In this invention, the ratio of load change to braking delay is used to quantify the load handling capacity per unit braking delay time, directly reflecting the load adaptability of the electronic braking system in the presence of response lag. For example, under heavy load conditions, if the braking delay is short and the load change rate is high, the efficiency ratio will be large, indicating strong system response capabilities. By calculating the efficiency ratio based on historical data, a load-braking delay correlation model can be established based on big data analysis, providing a quantitative basis for system optimization. For example, by comparing the efficiency ratio distribution of different vehicle models, it is possible to identify design flaws in the braking system.

[0057] Furthermore, based on the test scenario cj i The corresponding steering angle change data csa for the nth operating time period i,n , calculate the test scenario cj i The corresponding steering angular velocity in the nth operating time period is calculated as follows:

[0058]

[0059] Among them, acc i,n Represents the test scenario cj i The corresponding steering angular velocity of the nth operating time period, csa i,n-1 Represents the test scenario cj i The steering angle change data corresponding to the n-1th operating time period, Δt represents the time interval between the n-1th operating time period.

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

[0061] Specifically, based on the test scenario cj i The corresponding load-brake efficiency ratio eff of 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 of the corresponding n-th operating time period is calculated as follows:

[0062]

[0063] Among them, STE i,n Represents the test scenario cj i The steering-load coupling efficiency index corresponding to the nth operating time period, γ represents the preset attenuation coefficient;

[0064] In the present invention, an exponential decay function is used to characterize the inhibitory effect of steering on load-brake efficiency: the more violent the steering (|acc i,n The larger the value of |, the more pronounced the exponential decay of coupling efficiency. This is consistent with real-world physics: during sharp turns, the shift in the vehicle's center of gravity significantly impacts braking performance (e.g., increased wheel load on one side leads to uneven braking force). Combining load handling efficiency with steering dynamics allows for comprehensive quantification of the "braking-steering" coupling condition. 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 critical points in system performance.

[0065] Furthermore, the ideal braking response delay index λ is preset ideal and the minimum delay protection value λ min , and construct a benchmark score value, recorded as S base ,in, max() represents the maximum value function; the test scenario cj i The steering-load coupling efficiency index STE of the corresponding nth operating time period i,n Compared with the benchmark score S base Multiply and calculate the test scenario cj i The corresponding braking performance score of the nth operating time period is recorded as BPS i,n .

[0066] In this invention, the braking performance score multiplies the coupling efficiency index (reflecting actual operating conditions) by the baseline score (reflecting ideal performance) to form 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 baseline score, prompting the system to prioritize optimizing key performance bottlenecks.

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

[0068] Specifically, based on the test scenario cj i The corresponding braking performance score BPS of the nth operating time period i,n , calculate the test scenario cj i The calculation formula for the caliper wear corresponding to the nth operating time period is as follows:

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

[0070] Among them, CW i,n Represents the test scenario cj i The corresponding caliper wear degree of the nth operating time period, μ represents the preset wear coefficient;

[0071] Further, get the test scene cj i The corresponding caliper wear of all operating time periods and the calculation of the test scenario cj i The average caliper wear value is set, and the caliper wear threshold is preset. If the test scenario cj i The average caliper wear value of the caliper is less than the caliper wear threshold, then the test scenario cj is determined to be i For low wear scenarios; if the test scenario cj i The average caliper wear value of the caliper is greater than or equal to the caliper wear threshold, then the test scenario cj is determined to be i If it is a high-wear scenario, the simulation is stopped for maintenance, and in the next running time period, a low-wear scenario is selected for test simulation.

[0072] See also Figure 2 In the second embodiment, an electronic brake intelligent test simulation system based on big data analysis is provided, which 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 calculation and analysis module.

[0073] The data acquisition and collection construction module is used to obtain the braking response delay data, steering angle change data and load change data during the historical simulation process; obtain the operating time period corresponding to each test scenario, and construct a time series response data set;

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

[0075] The efficiency index and score calculation module calculates the steering-load coupling efficiency index for a single operating time period corresponding to a single test scenario based on the load-brake efficiency ratio and the steering angular velocity; constructs a baseline score value, and calculates the braking performance score in combination with the steering-load coupling efficiency index;

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

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

[0078] The data acquisition unit: based on the HIL simulation test platform, acquires the timing response data of the EMB system and the steering system during the historical simulation process, wherein the timing response data includes brake response delay data, steering angle change data, and load change data; constructs a test scenario set; evenly divides the simulation running time of each historical simulation test into a number of running time periods, and acquires the running time period corresponding to each test scenario, wherein one test scenario corresponds to at least one running time period;

[0079] The set construction unit normalizes the braking response delay data, steering angle change data and load change data of the operating time period corresponding to the test scenario, and constructs a time series response data set of the operating time period based on 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-brake efficiency ratio of the nth operating time period corresponding to the test scenario based on the brake response delay data and the load change data of the nth operating time period corresponding to the test scenario;

[0082] The angular velocity calculation unit calculates the steering angular velocity of the nth running time period corresponding to the test scenario based on the steering 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 of the nth operating time period corresponding to the test scenario based on the load-brake efficiency ratio and the steering angular velocity of 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 scoring value; multiplies the steering-load coupling efficiency index of the nth operating time period corresponding to the test scenario by the benchmark scoring value, and calculates 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 of 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: obtains the caliper wear of all operating time periods corresponding to the test scenario, calculates the average caliper wear of the test scenario, and presets a caliper wear threshold. If the average caliper wear of 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 of the test scenario is greater than or equal to the caliper wear threshold, the test scenario is determined to be a high-wear scenario, then the simulation is stopped, maintenance is performed, and in the next operating time period, the low-wear scenario is selected for test simulation.

[0089] Please refer to Table 1. In this third embodiment, an electronic brake intelligent test simulation method based on big data analysis is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0090] Test scenario: The preset test scenario is 1 (I=1), with a total of 3 operating time periods (N=3), a time interval Δt of 1 second, an error term ∈ of 0.1, a decay coefficient γ of 0.1, and an ideal braking response delay index λ ideal The minimum delay protection value λ is 0.3 seconds. min 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 changes 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] According to Table 1, it is calculated that 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 = 0.2107 is greater than or equal to 0.2, the test scenario cj1 is determined to be a high-wear scenario, and the simulation is stopped for maintenance. In the next operating time period, a low-wear scenario is selected for test simulation.

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

[0100] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An electronic brake intelligent test simulation method based on big data analysis, characterized in that: The method comprises the following steps: Step S1: Obtain brake response delay data, steering angle change data, and load change data during the historical simulation process; obtain the operating time period corresponding to each test scenario and construct a time series response data set; Step S2: Calculate the load-brake efficiency ratio and steering angular velocity for a single operating time period corresponding to a single test scenario; Step S3: Calculate the steering-load coupling efficiency index for a single operating period corresponding to a single test scenario based on the load-brake efficiency ratio and the steering angular velocity; construct a baseline score value, and calculate the braking performance score in combination with the steering-load coupling efficiency index; Step S4: Calculate the caliper wear for a single operating time period corresponding to a single test scenario based on the braking performance score; preset a threshold value and perform intelligent test simulation analysis.

2. The electronic brake intelligent test simulation method based on big data analysis according to claim 1 is characterized in that: The specific implementation process of step S1 includes: Based on the HIL simulation test platform, the time series response data of the EMB system and the steering system in the historical simulation process are obtained. The time series response data includes brake 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 represents the i-th test scenario, and I represents the total number of test scenarios; the simulation running time of each historical simulation test is evenly divided into several running time periods, and the running time period corresponding to each test scenario is obtained, wherein one test scenario corresponds to at least one running time period; For the test scene cj i The braking response delay data, steering angle change data and load change data of the corresponding operating time period are normalized, and a test scenario cj is constructed. i The time series response data set of the running time period is recorded as TSR i ={(brd i,n ,csa i,n ,lv i,n )|n∈[1,N]}, where brd i,n Represents the test scenario cj i The corresponding braking response delay data of the nth operating time period, csa i,n Represents the test scenario cj i The corresponding steering angle change data of the nth running time period, lv i,n Represents the test scenario cj i The corresponding load change data of the nth running time period, N represents the test scenario cj i The total number of corresponding running time periods.

3. The electronic brake intelligent test simulation method based on big data analysis according to claim 2 is characterized in that: The specific implementation process of step S2 includes: Based on the test scenario cj i The corresponding braking response delay data brd of the nth operating time period i,n and load change data lv i,n , calculate the test scenario cj i The corresponding load-brake efficiency ratio for the nth operating time period is calculated as follows: Among them, eff i,n Represents the test scenario cj i The corresponding load-brake efficiency ratio of the nth operating time period, ∈ represents the preset error term; Based on the test scenario cj i The corresponding steering angle change data csa for the nth operating time period i,n , calculate the test scenario cj i The corresponding steering angular velocity in the nth operating time period is calculated as follows: Among them, acc i,n Represents the test scenario cj i The corresponding steering angular velocity of the nth operating time period, csa i,n-1 Represents the test scenario cj i The steering angle change data corresponding to the n-1th operating time period, Δt represents the time interval between the n-1th operating time period.

4. The electronic brake intelligent test simulation method based on big data analysis according to claim 3 is characterized in that: The specific implementation process of step S3 includes: Based on the test scenario cj i The corresponding load-brake efficiency ratio eff of 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 of the corresponding n-th operating time period is calculated as follows: Among them, STE i,n Represents the test scenario cj i The steering-load coupling efficiency index corresponding to the nth operating time period, γ represents the preset attenuation coefficient; Preset ideal braking response delay index λ ideal and the minimum delay protection value λ min , and construct a benchmark score value, recorded as S base ,in, max() represents the maximum value function; the test scenario cj i The steering-load coupling efficiency index STE of the corresponding nth operating time period i,n Compared with the benchmark score S base Multiply and calculate the test scenario cj i The corresponding braking performance score of the nth operating time period is recorded as BPS i,n .

5. The electronic brake intelligent test simulation method based on big data analysis according to claim 4 is characterized in that: The specific implementation process of step S4 includes: Based on the test scenario cj i The corresponding braking performance score BPS of the nth operating time period i,n , calculate the test scenario cj i The calculation formula for the caliper wear corresponding to the nth operating time period is as follows: CW i,n =μ×(1-BPS i,n )×lv i,n ; Among them, CW i,n Represents the test scenario cj i The corresponding caliper wear degree of the nth operating time period, μ represents the preset wear coefficient; Get the test scene cj i The corresponding caliper wear of all operating time periods and the calculation of the test scenario cj i The average caliper wear value is set, and the caliper wear threshold is preset. If the test scenario cj i The average caliper wear value of the caliper is less than the caliper wear threshold, then the test scenario cj is determined to be i For low wear scenarios; if the test scenario cj i The average caliper wear value of the caliper is greater than or equal to the caliper wear threshold, then the test scenario cj is determined to be i If it is a high-wear scenario, the simulation is stopped for maintenance, and in the next running time period, a low-wear scenario is selected for test simulation.

6. An electronic brake intelligent test simulation system based on big data analysis, which executes an electronic brake intelligent test simulation method based on big data analysis according to any one of claims 1 to 5, 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 calculation and analysis module; The data acquisition and collection construction module is used to obtain the braking response delay data, steering angle change data and load change data during the historical simulation process; obtain the operating time period corresponding to each test scenario, and construct a time series response data set; The efficiency ratio and angular velocity calculation module calculates the load-brake efficiency ratio and steering angular velocity of a single operating time period corresponding to a single test scenario; The efficiency index and score calculation module calculates the steering-load coupling efficiency index for a single operating time period corresponding to a single test scenario based on the load-brake efficiency ratio and the steering angular velocity; constructs a baseline score value, and calculates the braking performance score in combination with the steering-load coupling efficiency index; The wear calculation and analysis module calculates the caliper wear in a single operating time period corresponding to a single test scenario based on the braking performance score; presets a threshold value, and performs intelligent test simulation analysis.

7. The electronic brake intelligent test simulation system based on big data analysis according to claim 6, characterized in that: The data acquisition and set construction module includes a data acquisition unit and a set construction unit; The data acquisition unit: based on the HIL simulation test platform, acquires the timing response data of the EMB system and the steering system during the historical simulation process, wherein the timing response data includes brake response delay data, steering angle change data, and load change data; constructs a test scenario set; evenly divides the simulation running time of each historical simulation test into a number of running time periods, and acquires the running time period corresponding to each test scenario, wherein one test scenario corresponds to at least one running time period; The set construction unit normalizes the braking response delay data, steering angle change data and load change data of the operating time period corresponding to the test scenario, and constructs a time series response data set of the operating time period based on the test scenario.

8. The electronic brake intelligent test simulation system based on big data analysis according to claim 7, 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-brake efficiency ratio of the nth operating time period corresponding to the test scenario based on the brake response delay data and the load change data of the nth operating time period corresponding to the test scenario; The angular velocity calculation unit calculates the steering angular velocity of the nth running time period corresponding to the test scenario based on the steering angle change data of the nth running time period corresponding to the test scenario.

9. The electronic brake intelligent test simulation system based on big data analysis according to claim 8, 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 of the nth operating time period corresponding to the test scenario based on the load-brake efficiency ratio and the steering angular velocity of 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 steering-load coupling efficiency index of the nth operating time period corresponding to the test scenario is multiplied by the benchmark score value to calculate the braking performance score of the nth operating time period corresponding to the test scenario.

10. The electronic brake intelligent test simulation system based on big data analysis according to claim 9, 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 of 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 analyzing unit is configured to obtain caliper wear for all operating time periods corresponding to a test scenario, calculate an average caliper wear for the test scenario, and preset 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 of the test scenario is greater than or equal to the caliper wear threshold, the test scenario is determined to be a high-wear scenario, and the simulation is stopped for maintenance. In the next operating time period, a low-wear scenario is selected for test simulation.

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