A simulation detection analysis method and system for vehicle body electronic stability control

Abstract

The application discloses a simulation detection analysis method and system for vehicle body electronic stability control, and belongs to the technical field of simulation detection; pressure limiting valve sealing data, wheel speed sensor data and ground grip sensing data of a vehicle body electronic stability control system are collected; a fault mode database of a single running time period corresponding to a test scene is constructed; the fault mode database of all running time periods is acquired, the probability that the test scene simultaneously appears pressure limiting valve sealing failure, wheel speed sensor drift failure and ground grip sensing abnormal failure in the next running time period is predicted; if the pressure limiting valve sealing failure, the wheel speed sensor drift failure and the ground grip sensing abnormal failure simultaneously appear, the braking stability parameter of the test scene in the next running time period is calculated; a preset threshold value is analyzed and early warning is performed, and finally, comprehensive monitoring of the vehicle body electronic stability state and adaptive adjustment of the simulation strategy are realized, and the accuracy and safety guarantee capability of simulation detection are improved.

Description

Technical Field

[0001] This invention relates to the field of simulation testing technology, specifically to a simulation testing and analysis method and system for vehicle electronic stability control. Background Technology

[0002] Electronic Stability Control (ESC), a crucial component of modern vehicle active safety technology, has evolved from the initial Anti-lock Braking System (ABS) and Traction Control System (TCS) to advanced ESC systems integrating multi-sensor information and real-time intervention in braking and power distribution. These technologies are widely applied in passenger cars, commercial vehicles, and special-purpose vehicles. With the increasing intelligence of vehicles, ESC systems are not only continuously optimized in terms of hardware architecture and control strategies but also gradually incorporating decision-making mechanisms based on big data and artificial intelligence. Existing ESC systems largely rely on performance verification under real-vehicle testing or limited scenario simulation conditions, focusing on stability performance under single faults or simple operating conditions. However, with the diversification of testing requirements and the increasing complexity of operating conditions, how to efficiently and accurately evaluate the stability control capabilities of ESC under various potentially high-risk fault combinations has become an important direction for current technological development.

[0003] Existing ESC technologies still have significant shortcomings in simulation detection and predictive analysis under multiple fault combinations: traditional simulation detection and analysis methods mostly use single fault modeling or fixed-condition testing, lacking the ability to collaboratively analyze key fault combinations such as pressure relief valve sealing failure, wheel speed sensor drift, and abnormal grip perception, making it difficult to comprehensively reflect the stability risks of vehicles under extreme conditions; current ESC simulation detection often lacks quantitative prediction of potential fault probabilities for the next operating period and probability-based early warning mechanisms, failing to achieve proactive prevention and control of high-risk conditions. These shortcomings not only limit the adaptive capabilities of ESC systems in complex traffic environments but also affect automakers' ability to conduct refined evaluation and optimization of stability control performance during product development and verification phases. Summary of the Invention

[0004] The purpose of this invention is to provide a simulation detection and analysis method and system for vehicle electronic stability control, 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 simulation detection and analysis method for vehicle electronic stability control includes the following steps: Step S1: Collect pressure relief valve sealing data, wheel speed sensor data, and grip perception data of the vehicle electronic stability control system during historical simulation detection; construct a set of test scenarios; Step S2: Construct a fault mode database for a single operating time period corresponding to the test scenario; Step S3: Obtain the fault mode database for all operating time periods, and predict the probability that the test scenario will simultaneously experience pressure relief valve sealing failure, wheel speed sensor drift, and grip perception abnormality in the next operating time period; Step S4: If the test scenario simultaneously experiences pressure relief valve sealing failure, wheel speed sensor drift, and grip perception abnormality in the next operating time period, calculate the braking stability parameters of the test scenario in the next operating time period; preset thresholds, analyze, and issue warnings.

[0007] As a preferred embodiment of the simulation detection and analysis method for vehicle electronic stability control described in this invention, simulation operation data of the vehicle electronic stability control system during historical simulation detection is collected through data processing and analysis technology. The simulation operation data includes pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data. The pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data are then cleaned and normalized.

[0008] Construct a set of test scenarios, denoted as TS = {ts} i |i∈[1,I]}, where ts i Let I represent the i-th test scenario and I represent the total number of test scenarios. Divide the simulation running time of each historical simulation test into several running time periods and obtain the running time period corresponding to each test scenario. Each test scenario corresponds to at least one running time period.

[0009] As a preferred embodiment of the simulation detection and analysis method for vehicle electronic stability control described in this invention, the i-th test scenario ts is respectively... i The pressure relief valve sealing data, wheel speed sensor data, and grip sensing data for the corresponding a-th operating time period are denoted as PLV. a (ts i WSS a (ts i ) and GP a (ts i );

[0010] Building test scenarios (TS) i The corresponding fault mode database for the a-th runtime period is as follows:

[0011] The preset pressure relief valve sealing data normal threshold θ, if PLV a (tsi If ) < θ, then determine the test scenario ts i The pressure relief valve seal failure occurred during the corresponding a-th operating time period and was recorded in the fault mode database;

[0012] Preset normal threshold range for wheel speed sensor data [α] min ,α max ],like Then determine the test scenario ts i A wheel speed sensor drift fault occurred during the corresponding a-th operating time period, and this fault mode was recorded in the fault mode database, where α min and α max These represent the lower and upper limits of the normal threshold range for wheel speed sensor data, respectively.

[0013] Preset normal threshold range for grip sensing data [β] min ,β max ],like Then determine the test scenario ts i A grip perception anomaly occurred during the corresponding a-th operating time period and was recorded in the fault mode database, where β min and β max These represent the lower and upper limits of the normal threshold range for grip perception data, respectively.

[0014] As a preferred embodiment of the simulation detection and analysis method for vehicle electronic stability control described in this invention, the test scenario ts is obtained. i A database of failure modes for all A operating time periods was compiled, and an observation dataset was constructed. The number of times pressure relief valve seal failure, wheel speed sensor drift failure, and grip perception anomaly failure occurred simultaneously was statistically analyzed to predict the test scenario ts. i The probability of simultaneous occurrence of pressure relief valve seal failure, wheel speed sensor drift, and grip sensing anomaly during the A+1th operating time period is as follows:

[0015] Test scenarios ts i The pressure relief valve sealing failure, wheel speed sensor drift failure, and grip force sensing abnormality failure during the A+1th operating time period are denoted as F1, F2, and F3, respectively.

[0016] The observation dataset is denoted as ODS. i ={(FA) 1,a ,FA 2,a ,FA 3,a ,μ a )|a∈[1,A]}, where A represents the test scenario ts i The corresponding total running time period, fS 1,a FA 2,a and FA3,a These represent the test scenarios (ts). i The corresponding faults in the pressure relief valve sealing failure, wheel speed sensor drift, and grip sensing abnormality during the a-th operating time period are μ. a This represents the friction coefficient during the a-th operating time period;

[0017] Calculate test scenario ts i The probability of simultaneous occurrence of pressure relief valve sealing failure, wheel speed sensor drift, and grip sensing abnormality during the (A+1)th operating time period is calculated using the following formula:

[0018]

[0019] Wherein, P(F1=1,F2=1,F3=1|ODS i ) represents the test scenario ts i The probability that a pressure relief valve seal failure, a wheel speed sensor drift failure, and a grip sensing abnormality will occur simultaneously during the A+1th operating time period, I(FA) 1,a =1,FA 2,a =1,FA 3,a =1) indicates the indicator function, if PLV a (ts i If ) < θ, then FA 1,a =1, if Then FA 2,a =1, if Then FA 3,a =1, ω a This represents the friction weighting factor. This indicates the preset attenuation factor.

[0020] It should be noted that in actual simulation testing or prediction, the (A+1)th running time period has not yet started, therefore the friction coefficient cannot be obtained in advance. Calculating the probability directly based on the future friction coefficient is logically flawed and impractical. Therefore, conditional probability inference must be made based on known historical data, by calculating the average friction coefficient of the previous A running time periods. This allows for the establishment of a baseline for the friction coefficient under this test scenario. This baseline represents the comprehensive characteristics of the historical state of this test scenario and helps determine the weight of different operating time periods in terms of operational consistency.

[0021] As a preferred embodiment of the simulation detection and analysis method for vehicle electronic stability control described in this invention, a preset probability threshold is used; if the test scenario ts i The probability P(F1=1,F2=1,F3=1|ODS) of simultaneous occurrence of pressure relief valve sealing failure, wheel speed sensor drift failure, and grip sensing abnormality during the A+1th operating time period.i If the probability is greater than the stated probability threshold, then the test scenario ts is determined. i If, during the (A+1)th operating time period, a pressure relief valve sealing failure, a wheel speed sensor drift failure, and a grip force sensing anomaly occur simultaneously, then calculate the test scenario ts. i The braking stability parameters for the (A+1)th operating time period are calculated using the following formula:

[0022]

[0023] in, This indicates the test scenario (ts). i Braking stability parameters, PLV during the (A+1)th operating time period A+1 (ts i WSS A+1 (ts i ) and GP A+1 (ts i ) represent the test scenarios ts respectively i In the A+1th operating time period, the pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data are used. λ represents the preset fusion factor, which is used to adjust the nonlinear gain effect of the pressure relief valve and wheel speed combination. δ represents the preset working condition sensitivity factor, which is used to control the suppression effect of grip force sensing on the stability prediction value.

[0024] It should be noted that in this formula, the numerator is the pressure relief valve sealing data PLV. A+1 (ts i ) and wheel speed sensor data WSS A+1 (ts i The product of () reflects the basic braking performance, and the fusion factor λ is used to adjust the nonlinear gain (λ greater than 1 amplifies the impact of faults, λ less than 1 smooths fluctuations); denominator: grip perception data GP A+1 (ts i ) through the exponential function exp(-δ×GP A+1 (ts i Construct a suppression term; the operating condition sensitivity factor δ controls the intensity of the suppression of stability by grip (the larger δ is, the greater the impact of grip reduction on stability). The more significant the negative impact, the better;

[0025] When GP A+1 (ts i A decrease in the denominator (e.g., due to slippery road conditions) leads to a decrease in the denominator value, resulting in... The significant decrease is consistent with the physical laws governing vehicle instability.

[0026] The ideal stability index BSP under preset normal conditions is set. tar And stability deviates from the threshold τ, if Then determine the test scenario ts i If the stability is poor and there is a high-risk fault combination during the A+1th running time period, an early warning will be issued to the relevant personnel and the simulation detection strategy will be adjusted.

[0027] A simulation detection and analysis system for vehicle electronic stability control includes: a data acquisition and set construction module, a database construction module, a fault prediction module, and a parameter calculation, analysis, and early warning module.

[0028] The data acquisition and collection construction module collects pressure relief valve sealing data, wheel speed sensor data, and grip perception data of the vehicle electronic stability control system during historical simulation testing; and constructs a set of test scenarios.

[0029] The database construction module: constructs a fault mode database for a single running time period corresponding to the test scenario;

[0030] The fault prediction module acquires a fault mode database for all operating time periods and predicts the probability that the pressure relief valve sealing failure, wheel speed sensor drift, and grip force sensing abnormality will occur simultaneously in the next operating time period of the test scenario.

[0031] The parameter calculation, analysis, and early warning module: If the test scenario simultaneously experiences a pressure relief valve sealing failure, a wheel speed sensor drift failure, and a grip force sensing abnormality failure in the next running time period, it calculates the braking stability parameters of the test scenario in the next running time period; presets a threshold, analyzes, and issues an early warning.

[0032] Furthermore, the data acquisition and collection construction module includes a data acquisition unit and a collection construction unit;

[0033] The data acquisition unit: uses data processing and analysis technology to collect simulation operation data of the vehicle electronic stability control system during historical simulation testing. The simulation operation data includes pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data. The pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data are then cleaned and normalized.

[0034] The set construction unit: constructs a set of test scenarios; evenly divides the simulation running time of each historical simulation test into several running time periods, and obtains the running time period corresponding to each test scenario, wherein one test scenario corresponds to no less than one running time period.

[0035] Furthermore, the database construction module includes a database construction unit;

[0036] The database construction unit constructs a fault mode database for the a-th running time period corresponding to the test scenario, specifically as follows: A normal threshold for pressure relief valve sealing data is preset. If the pressure relief valve sealing data is less than the normal threshold, it is determined that a pressure relief valve sealing failure fault exists in the a-th running time period corresponding to the test scenario, and this is recorded in the fault mode database. A normal threshold range for wheel speed sensor data is preset. If the wheel speed sensor data does not fall within the normal threshold range, it is determined that a wheel speed sensor drift fault exists in the a-th running time period corresponding to the test scenario, and this is recorded in the fault mode database. A normal threshold range for grip perception data is preset. If the grip perception data does not fall within the normal threshold range, it is determined that an abnormal grip perception fault exists in the a-th running time period corresponding to the test scenario, and this is recorded in the fault mode database.

[0037] Furthermore, the fault prediction module includes a fault prediction unit;

[0038] The fault prediction unit: acquires a fault mode database for all A operating time periods corresponding to the test scenario and constructs an observation dataset; counts the number of times pressure relief valve sealing failure, wheel speed sensor drift, and grip perception abnormality occur simultaneously, and predicts the probability that pressure relief valve sealing failure, wheel speed sensor drift, and grip perception abnormality occur simultaneously in the test scenario during the A+1th operating time period.

[0039] Furthermore, the parameter calculation and analysis early warning module includes a parameter calculation unit and an analysis early warning unit;

[0040] The parameter calculation unit: preset probability threshold, if the probability of the test scenario simultaneously experiencing pressure relief valve sealing failure, wheel speed sensor drift failure and grip force sensing abnormality failure during the A+1th running time period is greater than the probability threshold, then it is determined that the test scenario will simultaneously experience pressure relief valve sealing failure, wheel speed sensor drift failure and grip force sensing abnormality failure during the A+1th running time period, and then the braking stability parameters of the test scenario during the A+1th running time period are calculated;

[0041] The analysis and early warning unit: presets an ideal stability index and a stability deviation threshold under normal conditions. If the absolute value of the difference between the braking stability parameter and the ideal stability index is greater than the stability deviation threshold, it determines that the test scenario has poor stability in the A+1th running time period and there is a high-risk fault combination. Then, it issues an early warning to relevant personnel and adjusts the simulation detection strategy.

[0042] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: The simulation detection and analysis method and system for vehicle electronic stability control provided by this invention collects and processes pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data to construct a set of test scenarios. This enables high-quality modeling and feature summarization of historical simulation data, laying a unified and reliable foundation for subsequent analysis. By constructing a fault mode database for each operating time period of the test scenarios, multi-dimensional fault identification and classification recording are achieved, improving the accuracy and traceability of fault judgment. By statistically analyzing the co-occurrence frequency of high-risk fault combinations in historical operating time periods, the probability of high-risk fault combinations in the next time period is predicted, enabling quantitative analysis and early perception of fault trends, providing a scientific basis for risk prevention and control. When a high-risk probability exceeds a threshold, braking stability parameters are calculated and dynamically evaluated and intelligently warned based on preset thresholds. This achieves comprehensive monitoring of the vehicle electronic stability status and adaptive adjustment of simulation strategies, ultimately significantly improving the accuracy, foresight, and safety assurance capabilities of simulation detection. Attached Figure Description

[0043] 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.

[0044] Figure 1 This is a schematic diagram illustrating the steps of a simulation detection and analysis method for vehicle electronic stability control according to the present invention.

[0045] Figure 2 This is a schematic diagram of the structure of a simulation detection and analysis system for vehicle electronic stability control according to the present invention. Detailed Implementation

[0046] 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.

[0047] Please see Figure 1 In this first embodiment: a simulation detection and analysis method for vehicle electronic stability control is provided, which includes the following steps:

[0048] Step S1: Collect the pressure relief valve sealing data, wheel speed sensor data, and grip perception data of the vehicle electronic stability control system during historical simulation testing; construct a set of test scenarios.

[0049] Specifically, through data processing and analysis technology, simulation operation data of the vehicle electronic stability control system during historical simulation testing is collected. The simulation operation data includes pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data. The pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data are then cleaned and normalized.

[0050] Construct a set of test scenarios, denoted as TS = {ts} i |i∈[1,I]}, where ts i Let I represent the i-th test scenario and I represent the total number of test scenarios. Divide the simulation running time of each historical simulation test into several running time periods and obtain the running time period corresponding to each test scenario. Each test scenario corresponds to at least one running time period.

[0051] Step S2: Construct a fault mode database for a single running time period corresponding to the test scenario.

[0052] Specifically, the i-th test scenario ts i The pressure relief valve sealing data, wheel speed sensor data, and grip sensing data for the corresponding a-th operating time period are denoted as PLV. a (ts i WSS a (ts i ) and GP a (ts i );

[0053] Building test scenarios (TS) i The corresponding fault mode database for the a-th runtime period is as follows:

[0054] The preset pressure relief valve sealing data normal threshold θ, if PLV a (ts i If ) < θ, then determine the test scenario ts i The pressure relief valve seal failure occurred during the corresponding a-th operating time period and was recorded in the fault mode database;

[0055] Preset normal threshold range for wheel speed sensor data [α] min ,α max ],like Then determine the test scenario ts i A wheel speed sensor drift fault occurred during the corresponding a-th operating time period, and this fault mode was recorded in the fault mode database, where α min and α max These represent the lower and upper limits of the normal threshold range for wheel speed sensor data, respectively.

[0056] Preset normal threshold range for grip sensing data [β]min ,β max ],like Then determine the test scenario ts i A grip perception anomaly occurred during the corresponding a-th operating time period and was recorded in the fault mode database, where β min and β max These represent the lower and upper limits of the normal threshold range for grip perception data, respectively.

[0057] Step S3: Obtain the fault mode database for all running time periods and predict the probability that the pressure relief valve sealing failure, wheel speed sensor drift failure, and grip perception abnormality failure will occur simultaneously in the next running time period of the test scenario.

[0058] Specifically, obtain the test scenario (TS). i A database of failure modes for all A operating time periods was compiled, and an observation dataset was constructed. The number of times pressure relief valve seal failure, wheel speed sensor drift failure, and grip perception anomaly failure occurred simultaneously was statistically analyzed to predict the test scenario ts. i The probability of simultaneous occurrence of pressure relief valve seal failure, wheel speed sensor drift, and grip sensing anomaly during the A+1th operating time period is as follows:

[0059] Test scenarios ts i The pressure relief valve sealing failure, wheel speed sensor drift failure, and grip force sensing abnormality failure during the A+1th operating time period are denoted as F1, F2, and F3, respectively.

[0060] The observation dataset is denoted as ODS. i ={(FA) 1,a ,FA 2,a ,FA 3,a ,μ a )|a∈[1,A]}, where A represents the test scenario ts i The corresponding total running time period, FA 1,a FA 2,a and FA 3,a These represent the test scenarios (ts). i The corresponding faults in the pressure relief valve sealing failure, wheel speed sensor drift, and grip sensing abnormality during the a-th operating time period are μ. a This represents the friction coefficient during the a-th operating time period;

[0061] Calculate test scenario ts i The probability of simultaneous occurrence of pressure relief valve sealing failure, wheel speed sensor drift, and grip sensing abnormality during the (A+1)th operating time period is calculated using the following formula:

[0062]

[0063] Wherein, P(F1=1,F2=1,F3=1|ODS i ) represents the test scenario ts i The probability that a pressure relief valve seal failure, a wheel speed sensor drift failure, and a grip sensing abnormality will occur simultaneously during the A+1th operating time period, I(FA) 1,a =1,FA 2,a =1,FA 3,a =1) indicates the indicator function, if PLV a (ts i If ) < θ, then FA 1,a =1, if Then FA 2,a =1, if Then FA 3,a =1, ω a This represents the friction weighting factor. This indicates the preset attenuation factor.

[0064] In this invention, the friction coefficient is correlated with environmental conditions (such as road surface slipperiness) to make the prediction more in line with the actual scenario. If the current friction coefficient differs greatly from the historical average (such as suddenly encountering an ice surface), the reference weight of the historical data is reduced to avoid unconventional conditions interfering with the accuracy of the prediction.

[0065] Step S4: If the test scenario simultaneously experiences pressure relief valve sealing failure, wheel speed sensor drift, and grip perception abnormality in the next running time period, calculate the braking stability parameters of the test scenario in the next running time period; preset thresholds, analyze, and issue warnings.

[0066] Specifically, a preset probability threshold is set for the test scenario ts. i The probability P(F1=1,F2=1,F3=1|ODS) of simultaneous occurrence of pressure relief valve sealing failure, wheel speed sensor drift failure, and grip sensing abnormality during the A+1th operating time period. i If the probability is greater than the stated probability threshold, then the test scenario ts is determined. i If, during the (A+1)th operating time period, a pressure relief valve sealing failure, a wheel speed sensor drift failure, and a grip force sensing anomaly occur simultaneously, then calculate the test scenario ts. i The braking stability parameters for the (A+1)th operating time period are calculated using the following formula:

[0067]

[0068] in, This indicates the test scenario (ts). i Braking stability parameters, PLV during the (A+1)th operating time period A+1(ts i WSS A+1 (ts i ) and GP A+1 (ts i ) represent the test scenarios ts respectively i In the A+1th operating time period, the pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data are used. λ represents the preset fusion factor, which is used to adjust the nonlinear gain effect of the pressure relief valve and wheel speed combination. δ represents the preset working condition sensitivity factor, which is used to control the suppression effect of grip force sensing on the stability prediction value.

[0069] The ideal stability index BSP under preset normal conditions is set. tar And stability deviates from the threshold τ, if Then determine the test scenario ts i If the stability is poor and there is a high-risk fault combination during the A+1th running time period, an early warning will be issued to the relevant personnel and the simulation detection strategy will be adjusted.

[0070] In this invention, simply determining whether a fault mode exists does not directly reflect the dynamic response of the vehicle under that condition. This is because the impact of fault combinations on the vehicle is not a simple linear superposition; there may be coupling or amplification effects (for example, pressure relief valves and wheel speed drift can amplify instability risks under low friction). By judging the deviation of braking stability parameters from ideal stability indicators, a quantitative and safety boundary analysis of the impact of the aforementioned fault combinations is performed. This analysis directly guides the control strategy for simulation testing (such as adjusting braking force distribution and ESC intervention strategies), forming a closed-loop detection system. Furthermore, screening fault modes using conditional probability before proceeding to detailed stability analysis under key operating conditions reduces computational burden and improves efficiency.

[0071] In summary, identifying the three fault modes first is to quickly identify potentially high-risk operating conditions; calculating braking stability parameters based on the fused data of the three faults is to quantitatively analyze the actual impact of combined faults on vehicle function and guide the adjustment of simulation testing strategies.

[0072] Please see Figure 2 In this second embodiment: a simulation detection and analysis system for vehicle electronic stability control is provided. The system includes: a data acquisition and collection construction module, a database construction module, a fault prediction module, and a parameter calculation, analysis, and early warning module.

[0073] The data acquisition and collection construction module collects pressure relief valve sealing data, wheel speed sensor data, and grip perception data of the vehicle electronic stability control system during historical simulation testing; and constructs a set of test scenarios.

[0074] The database construction module: constructs a fault mode database for a single running time period corresponding to the test scenario;

[0075] The fault prediction module acquires a fault mode database for all operating time periods and predicts the probability that the pressure relief valve sealing failure, wheel speed sensor drift, and grip force sensing abnormality will occur simultaneously in the next operating time period of the test scenario.

[0076] The parameter calculation, analysis, and early warning module: If the test scenario simultaneously experiences a pressure relief valve sealing failure, a wheel speed sensor drift failure, and a grip force sensing abnormality failure in the next running time period, it calculates the braking stability parameters of the test scenario in the next running time period; presets a threshold, analyzes, and issues an early warning.

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

[0078] The data acquisition unit: uses data processing and analysis technology to collect simulation operation data of the vehicle electronic stability control system during historical simulation testing. The simulation operation data includes pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data. The pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data are then cleaned and normalized.

[0079] The set construction unit: constructs a set of test scenarios; evenly divides the simulation running time of each historical simulation test into several running time periods, and obtains the running time period corresponding to each test scenario, wherein one test scenario corresponds to no less than one running time period.

[0080] Furthermore, the database construction module includes a database construction unit;

[0081] The database construction unit constructs a fault mode database for the a-th running time period corresponding to the test scenario, specifically as follows: A normal threshold for pressure relief valve sealing data is preset. If the pressure relief valve sealing data is less than the normal threshold, it is determined that a pressure relief valve sealing failure fault exists in the a-th running time period corresponding to the test scenario, and this is recorded in the fault mode database. A normal threshold range for wheel speed sensor data is preset. If the wheel speed sensor data does not fall within the normal threshold range, it is determined that a wheel speed sensor drift fault exists in the a-th running time period corresponding to the test scenario, and this is recorded in the fault mode database. A normal threshold range for grip perception data is preset. If the grip perception data does not fall within the normal threshold range, it is determined that an abnormal grip perception fault exists in the a-th running time period corresponding to the test scenario, and this is recorded in the fault mode database.

[0082] Furthermore, the fault prediction module includes a fault prediction unit;

[0083] The fault prediction unit: acquires a fault mode database for all A operating time periods corresponding to the test scenario and constructs an observation dataset; counts the number of times pressure relief valve sealing failure, wheel speed sensor drift, and grip perception abnormality occur simultaneously, and predicts the probability that pressure relief valve sealing failure, wheel speed sensor drift, and grip perception abnormality occur simultaneously in the test scenario during the A+1th operating time period.

[0084] Furthermore, the parameter calculation and analysis early warning module includes a parameter calculation unit and an analysis early warning unit;

[0085] The parameter calculation unit: preset probability threshold, if the probability of the test scenario simultaneously experiencing pressure relief valve sealing failure, wheel speed sensor drift failure and grip force sensing abnormality failure during the A+1th running time period is greater than the probability threshold, then it is determined that the test scenario will simultaneously experience pressure relief valve sealing failure, wheel speed sensor drift failure and grip force sensing abnormality failure during the A+1th running time period, and then the braking stability parameters of the test scenario during the A+1th running time period are calculated;

[0086] The analysis and early warning unit: presets an ideal stability index and a stability deviation threshold under normal conditions. If the absolute value of the difference between the braking stability parameter and the ideal stability index is greater than the stability deviation threshold, it determines that the test scenario has poor stability in the A+1th running time period and there is a high-risk fault combination. Then, it issues an early warning to relevant personnel and adjusts the simulation detection strategy.

[0087] In this third embodiment, a simulation detection and analysis method for vehicle electronic stability control is provided. To verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0088] Assume the total number of running time periods A = 5, and the attenuation factor... friction coefficient μ a =[0.85,0.82,0.3,0.8,0.78], during the first and second running periods, the pressure relief valve sealing failure, wheel speed sensor drift, and grip force sensing abnormality occurred simultaneously;

[0089]

[0090] ω1=exp(-0.8×|0.85-0.71|)=exp(-0.112)=0.894;

[0091] ω2=exp(-0.8×|0.82-0.71|)=exp(-0.088)=0.916;

[0092] ω1=exp(-0.8×|0.3-0.71|)=exp(-0.328)=0.72;

[0093] ω1=exp(-0.8×|0.8-0.71|)=exp(-0.072)=0.93;

[0094] ω1=exp(-0.8×|0.78-0.71|)=exp(-0.056)=0.945;

[0095]

[0096] With a preset probability threshold of 0.4, then P(F1=1,F2=1,F3=1|ODS i If the probability is greater than the probability threshold, then calculate the test scenario ts. i Braking stability parameters during the (A+1)th operating time period;

[0097] Assuming PLV A+1 (ts i ) = 0.4, WSS A+1 (ts i ) = 1.8, GP A+1 (ts i =0.2, fusion factor λ = 1.3, operating condition sensitivity factor δ = 1, ideal stability index BSP under normal conditions tar =0.8, stability deviation threshold τ=0.3, substitute into the formula to calculate:

[0098]

[0099] Then determine the test scenario ts i If the stability is poor and there is a high-risk fault combination during the A+1th running time period, an early warning will be issued to the relevant personnel and the simulation detection strategy will be adjusted.

[0100] 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.

[0101] 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 simulation test analysis method for vehicle body electronic stability control, characterized by, The method includes the following steps: Step S1: Collect pressure relief valve sealing data, wheel speed sensor data, and grip perception data of the vehicle electronic stability control system during historical simulation testing; construct a set of test scenarios; Step S2: Construct a fault mode database for a single runtime period corresponding to the test scenario; Step S3: Obtain the fault mode database for all running time periods and predict the probability that the pressure relief valve sealing failure, wheel speed sensor drift failure, and grip force sensing abnormality failure will occur simultaneously in the next running time period of the test scenario. Step S4: If the test scenario simultaneously experiences pressure relief valve sealing failure, wheel speed sensor drift, and grip perception abnormality in the next running time period, calculate the braking stability parameters of the test scenario in the next running time period; preset thresholds, analyze, and issue warnings. The specific implementation process of step S4 includes: Preset probability threshold, if the test scenario The probability of simultaneous occurrence of pressure relief valve seal failure, wheel speed sensor drift, and grip sensing anomaly during the A+1th operating time period. If the probability is greater than the stated probability threshold, then the test scenario is determined. If, during the A+1th operating period, a pressure relief valve sealing failure, a wheel speed sensor drift failure, and a grip force sensing anomaly all occur simultaneously, then calculate the test scenario. The braking stability parameters for the (A+1)th operating time period are calculated using the following formula: ； in, Indicates the test scenario Braking stability parameters during the (A+1)th operating time period , and These represent the test scenarios. The pressure relief valve sealing data, wheel speed sensor data, and grip sensing data during the A+1th operating time period. This represents the preset fusion factor. This represents the preset operating condition sensitivity factor. This represents the i-th test scenario. , and These represent the test scenarios. Pressure relief valve seal failure during the A+1th operating period, test scenario Wheel speed sensor drift fault and test scenario in the A+1th running time period An abnormal grip perception fault occurred during the A+1th operating time period; ideal stability index in the preset normal state and a stability deviation threshold value , if , then determine the test scene if the stability is poor in the A+1th runtime period and there is a high-risk fault combination, an early warning is issued to the relevant staff, and the simulation detection strategy is adjusted.

2. A simulation test analysis method for vehicle body electronic stability control according to claim 1, characterized in that, The specific implementation process of step S1 includes: Through data processing and analysis technology, simulation operation data of the vehicle electronic stability control system during historical simulation testing is collected. The simulation operation data includes pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data. The pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data are then cleaned and normalized. Construct a set of test scenarios, denoted as . ,in, Let I represent the i-th test scenario and I represent the total number of test scenarios. Divide the simulation running time of each historical simulation test into several running time periods and obtain the running time period corresponding to each test scenario. Each test scenario corresponds to at least one running time period.

3. A simulation test analysis method for vehicle body electronic stability control according to claim 2, characterized in that, The specific implementation process of step S2 includes: The i-th test scenario is denoted as The pressure-limiting valve sealing data, wheel speed sensor data and grip perception data for the corresponding a-th runtime period are denoted as , and ; Constructing test scenarios a corresponding failure mode database for the a-th period of runtime, as follows: Pre-set pressure limiting valve sealing data normal threshold If , it is judged that the test scene corresponding to the a th running time period exists a pressure limiting valve sealing failure fault, and is recorded in the fault mode database. Preset wheel speed sensor data normal threshold range ,like Then determine the test scenario. A wheel speed sensor drift fault occurred during the corresponding a-th operating time period, and this fault mode was recorded in the fault mode database. and These represent the lower and upper limits of the normal threshold range for wheel speed sensor data, respectively. Preset normal threshold range for grip sensing data ,like Then determine the test scenario. A grip perception anomaly occurred during the corresponding a-th operating time period, and this was recorded in the fault mode database. and These represent the lower and upper limits of the normal threshold range for grip perception data, respectively.

4. A simulation test analysis method for vehicle body electronic stability control according to claim 3, characterized in that, The specific implementation process of step S3 includes: Get test scenarios A database of failure modes for all A operating time periods was compiled, and an observation dataset was constructed. The number of times pressure relief valve seal failure, wheel speed sensor drift failure, and grip sensing anomaly occurred simultaneously was statistically analyzed to predict test scenarios. The probability of simultaneous occurrence of pressure relief valve seal failure, wheel speed sensor drift, and grip sensing anomaly during the A+1th operating time period is as follows: The test scenarios are respectively The pressure-limiting valve seal failure fault, the wheel speed sensor drift fault, and the grip perception anomaly fault are recorded at the A+1th runtime period , and ; The observation dataset is denoted as Where A represents the test scenario. The corresponding total running time period, , and These represent the test scenarios. The corresponding faults during the a-th operating time period are: pressure relief valve sealing failure, wheel speed sensor drift, and grip sensing abnormality. This represents the friction coefficient during the a-th operating time period; Calculate test scenarios The probability of simultaneous occurrence of pressure relief valve sealing failure, wheel speed sensor drift, and grip sensing abnormality during the (A+1)th operating time period is calculated using the following formula: ； ； in, Indicates the test scenario The probability of simultaneous occurrence of pressure relief valve sealing failure, wheel speed sensor drift failure, and grip sensing abnormality during the A+1th operating time period. Indicates an indicator function, if ,but ,like ,but ,like ,but , This represents the friction weighting factor. This indicates the preset attenuation factor.

5. A simulation detection and analysis system for vehicle electronic stability control, executing the simulation detection and analysis method for vehicle electronic stability control as described in any one of claims 1-4, characterized in that, The system includes: a data acquisition and collection construction module, a database construction module, a fault prediction module, and a parameter calculation, analysis, and early warning module; The data acquisition and collection construction module collects pressure relief valve sealing data, wheel speed sensor data, and grip perception data of the vehicle electronic stability control system during historical simulation testing; and constructs a set of test scenarios. The database construction module: constructs a fault mode database for a single running time period corresponding to the test scenario; The fault prediction module acquires a fault mode database for all operating time periods and predicts the probability that the test scenario will simultaneously experience pressure relief valve sealing failure, wheel speed sensor drift, and grip force sensing abnormality in the next operating time period. The parameter calculation, analysis, and early warning module: If the test scenario simultaneously experiences a pressure relief valve sealing failure, a wheel speed sensor drift failure, and a grip force sensing abnormality failure in the next running time period, it calculates the braking stability parameters of the test scenario in the next running time period; presets a threshold, analyzes, and issues an early warning.

6. A simulation test analysis system for electronic stability control of a vehicle body according to claim 5, characterized in that: The data acquisition and collection construction module includes a data acquisition unit and a collection construction unit; The data acquisition unit: uses data processing and analysis technology to collect simulation operation data of the vehicle electronic stability control system during historical simulation testing. The simulation operation data includes pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data. The pressure relief valve sealing data, wheel speed sensor data, and grip force sensing data are then cleaned and normalized. The set construction unit: constructs a set of test scenarios; The simulation runtime of each historical simulation test is evenly divided into several runtime periods, and the runtime period corresponding to each test scenario is obtained. Each test scenario corresponds to at least one runtime period.

7. A simulation test analysis system for electronic stability control of a vehicle body according to claim 6, characterized in that: The database construction module includes database construction units; The database construction unit constructs a fault mode database for the a-th running time period corresponding to the test scenario, specifically as follows: a normal threshold for the pressure relief valve sealing data is preset. If the pressure relief valve sealing data is less than the normal threshold for the pressure relief valve sealing data, it is determined that there is a pressure relief valve sealing failure fault in the a-th running time period corresponding to the test scenario, and it is recorded in the fault mode database. The normal threshold range for wheel speed sensor data is preset. If the wheel speed sensor data does not fall within the normal threshold range, it is determined that there is a wheel speed sensor drift fault in the a-th running time period corresponding to the test scenario, and it is recorded in the fault mode database. A normal threshold range for grip perception data is preset. If the grip perception data does not fall within the normal threshold range, it is determined that there is a grip perception abnormality fault in the a-th running time period corresponding to the test scenario, and it is recorded in the fault mode database.

8. A simulation test analysis system for vehicle body electronic stability control according to claim 7, characterized in that: The fault prediction module includes a fault prediction unit; The fault prediction unit: acquires a fault mode database for all A running time periods corresponding to the test scenario, and constructs an observation dataset; The number of times that pressure relief valve sealing failure, wheel speed sensor drift failure, and grip force sensing abnormality occur simultaneously is statistically analyzed, and the probability of these three failures occurring simultaneously in the A+1th running time period of the test scenario is predicted.

9. A simulation test analysis system for electronic stability control of a vehicle body according to claim 8, characterized in that: The parameter calculation and analysis early warning module includes a parameter calculation unit and an analysis early warning unit; The parameter calculation unit: preset probability threshold, if the probability of the test scenario simultaneously experiencing pressure relief valve sealing failure, wheel speed sensor drift failure and grip force sensing abnormality failure during the A+1th running time period is greater than the probability threshold, then it is determined that the test scenario will simultaneously experience pressure relief valve sealing failure, wheel speed sensor drift failure and grip force sensing abnormality failure during the A+1th running time period, and then the braking stability parameters of the test scenario during the A+1th running time period are calculated; The analysis and early warning unit: presets an ideal stability index and a stability deviation threshold under normal conditions. If the absolute value of the difference between the braking stability parameter and the ideal stability index is greater than the stability deviation threshold, it determines that the test scenario has poor stability in the A+1th running time period and there is a high-risk fault combination. Then, it issues an early warning to relevant personnel and adjusts the simulation detection strategy.

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