Simulation detection analysis method and system for vehicle body electronic stability control

Abstract

The invention 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 road holding sensing data of the vehicle body electronic stability control system are collected; constructing a fault mode database of a single operation time period corresponding to the test scene; obtaining a fault mode database of all the operation time periods, and predicting the probability that a pressure limiting valve sealing failure fault, a wheel speed sensor drifting fault and a road holding force sensing abnormal fault occur at the same time in the next operation time period of the test scene; if the pressure-limiting valve sealing failure fault, the wheel speed sensor drifting fault and the road holding force sensing abnormal fault occur at the same time, brake stability parameters of the test scene in the next operation time period are calculated; according to the method, the electronic stability state of the vehicle body is monitored, a threshold value is preset, analysis and early warning are carried out, comprehensive monitoring of the electronic stability state of the vehicle body and simulation strategy self-adaptive adjustment are finally achieved, and the accuracy and safety guarantee capacity of simulation detection are improved.

Description

Technical Field

[0001] The present invention relates to the field of simulation detection technology, and in particular to a simulation detection analysis method and system for vehicle body electronic stability control. Background Art

[0002] Electronic Stability Control (ESC), a key 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 that integrate multi-sensor information and intervene in braking and power distribution in real time. These technologies have been widely used in passenger cars, commercial vehicles, and specialized vehicles. With the increasing intelligence of vehicles, ESC systems are not only continuously optimized in terms of hardware architecture and control strategies, but are also gradually incorporating decision-making support mechanisms based on big data and artificial intelligence. Existing ESC systems often rely on performance verification under real-world 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 complexity of operating conditions, how to efficiently and accurately evaluate the stability control capabilities of ESC under a variety of potentially high-risk fault combinations has become a key direction for current technological development.

[0003] Existing ESC-related technologies still have significant deficiencies in simulation testing and predictive analysis under multiple fault combinations: Traditional simulation testing and analysis methods often use single-fault modeling or fixed-condition testing, lacking the ability to collaboratively analyze key fault combinations such as pressure-limiting valve seal failure, wheel speed sensor drift, and abnormal grip perception, making it difficult to fully reflect the vehicle's stability risks under extreme conditions. Current ESC simulation testing often lacks quantitative predictions of potential failure probabilities in the next operating time period and probability-based early warning mechanisms, making it impossible to proactively prevent and control high-risk conditions. These deficiencies not only limit the ESC system's adaptability in complex traffic environments but also hinder automakers' ability to fine-tune and optimize stability control performance during product development and verification. Summary of the Invention

[0004] The object of the present invention is to provide a simulation detection and analysis method and system for vehicle body electronic stability control, so as 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] A simulation detection and analysis method for vehicle body electronic stability control, the method comprising the following steps: step S1: collecting pressure limiting valve sealing data, wheel speed sensor data and grip perception data of the vehicle body electronic stability control system during historical simulation detection processes; constructing a test scenario set; step S2: constructing a fault mode database for a single operating time period corresponding to the test scenario; step S3: obtaining the fault mode database for all operating time periods, and predicting the probability that the test scenario will simultaneously experience a pressure limiting valve sealing failure fault, a wheel speed sensor drift fault and a grip perception abnormality fault in the next operating time period; step S4: if the test scenario simultaneously experiences a pressure limiting valve sealing failure fault, a wheel speed sensor drift fault and a grip perception abnormality fault in the next operating time period, then calculating the braking stability parameters of the test scenario in the next operating time period; presetting a threshold, analyzing and issuing an early warning.

[0007] As a preferred embodiment of the simulation detection and analysis method for vehicle electronic stability control described in the present invention, data processing and analysis technology is used to collect simulation operation data of the vehicle electronic stability control system during historical simulation detection processes, wherein the simulation operation data includes pressure limiting valve sealing data, wheel speed sensor data, and grip perception data. The pressure limiting valve sealing data, the wheel speed sensor data, and the grip perception data are cleaned and normalized.

[0008] Construct a test scenario set, denoted as TS = {ts i |i∈[1,I]}, where ts i represents the i-th test scenario, and I represents the total number of test scenarios; the simulation running time of each historical simulation detection is evenly divided into several running time periods, and the running time period corresponding to each test scenario is obtained, where one test scenario corresponds to no less than one running time period.

[0009] As a preferred solution of the simulation detection and analysis method for vehicle body electronic stability control described in the present invention, the i-th test scene ts i The corresponding pressure limiting valve sealing data, wheel speed sensor data and grip perception data of the ath operating time period are recorded as PLV a (ts i ),WSS a (ts i ) and GP a (ts i );

[0010] Build test scenario ts i The corresponding failure mode database for the ath running time period is as follows:

[0011] Preset the normal threshold value θ of the pressure limiting valve sealing data, if PLV a (tsi )<θ, then determine the test scenario ts i The corresponding a-th operating time period has a pressure limiting valve seal failure fault, which is recorded in the fault mode database;

[0012] Preset wheel speed sensor data normal threshold range [α min ,α max ],like Then determine the test scenario ts i The corresponding a-th operating time period has a wheel speed sensor drift fault and is recorded in the fault mode database, where α min and α max Respectively represent the lower and upper limits of the normal threshold range of wheel speed sensor data;

[0013] Preset normal threshold range of grip perception data [β min ,β max ],like Then determine the test scenario ts i The corresponding a-th operating time period has a grip perception abnormality fault and is recorded in the fault mode database, where β min and β max They represent the lower and upper limits of the normal threshold range of grip perception data respectively.

[0014] As a preferred solution of the simulation detection and analysis method for vehicle body electronic stability control described in the present invention, the test scene ts is obtained. i The corresponding fault mode database of all A operating time periods is constructed, and an observation data set is constructed; the number of simultaneous occurrences of pressure limiting valve seal failure, wheel speed sensor drift failure, and grip perception abnormality failure is counted, and the test scenario ts is predicted. i The probability of the pressure limiting valve seal failure fault, wheel speed sensor drift fault, and grip sensor abnormality fault occurring simultaneously during the A+1th operating time period is as follows:

[0015] The test scenario ts i The pressure limiting valve seal failure fault, wheel speed sensor drift fault, and grip perception abnormality fault in the A+1th operating time period are recorded as F1, F2, and F3;

[0016] The observation data set 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 Represents the test scenario ts respectively i The corresponding pressure limiting valve seal failure fault, wheel speed sensor drift fault and grip perception abnormal fault in the ath operating time period, μ a represents the friction coefficient of the ath operating time period;

[0017] Calculate test scenario ts i The probability of the pressure limiting valve seal failure, wheel speed sensor drift failure, and grip sensor abnormality failure occurring simultaneously during the A+1th operating time period is calculated as follows:

[0018]

[0019] Among them, P(F1=1,F2=1,F3=1|ODS i ) represents the test scenario ts i The probability of simultaneous occurrence of pressure limiting valve seal failure, wheel speed sensor drift failure, and grip sensor abnormality failure during the A+1th operating time period, I(FA 1,a =1,FA 2,a =1,FA 3,a =1) represents the indicator function, if PLV a (ts i )<θ, then FA 1,a =1, if Then FA 2,a =1, if Then FA 3,a =1,ω a represents the friction weight factor, Indicates the preset attenuation factor.

[0020] It should be noted that in actual simulation detection or prediction, the A+1th operating period has not yet begun, so the friction coefficient cannot be obtained in advance. If the probability is calculated directly based on the future friction coefficient, it is logically invalid and lacks feasibility. Therefore, conditional probability inference must be made based on known historical data, by calculating the average friction coefficient of the first A operating periods. A friction coefficient baseline for this test scenario can be formed. This baseline represents the comprehensive characteristics of the test scenario's historical state and helps determine the weight of different operating time periods in terms of working condition consistency.

[0021] As a preferred solution of the simulation detection and analysis method for vehicle body electronic stability control described in the present invention, a probability threshold is preset. If the test scenario ts i The probability P(F1=1,F2=1,F3=1|ODS) that the pressure limiting valve seal failure fault, wheel speed sensor drift fault and grip sensor abnormality fault occur simultaneously in the A+1th operating time periodi ) is greater than the probability threshold, then the test scenario ts is determined i In the A+1th operating time period, the pressure limiting valve seal failure, wheel speed sensor drift failure, and grip perception abnormality failure will occur simultaneously. Then the test scenario ts is calculated. i The braking stability parameter in the A+1th operating time period is calculated as follows:

[0022]

[0023] in, Represents the test scenario ts i Braking stability parameter in the A+1th operating time period, PLV A+1 (ts i ),WSS A+1 (ts i ) and GP A+1 (ts i ) represent the test scenarios ts i The pressure limiting valve sealing data, wheel speed sensor data, and grip perception data for the A+1th operating time period. λ represents a preset fusion factor, which is used to adjust the nonlinear gain effect of the pressure limiting valve and wheel speed combination. δ represents a preset operating condition sensitivity factor, which is used to control the inhibitory effect of grip perception on the stability prediction value.

[0024] It should be noted that in this formula, the numerator: pressure limiting valve sealing data PLV A+1 (ts i ) and wheel speed sensor data WSS A+1 (ts i ) reflects the basic braking efficiency, and the fusion factor λ is used to adjust the nonlinear gain (λ is greater than 1, the fault effect is amplified, and λ is less than 1, the fluctuation is smoothed); the denominator part: the grip perception data GP A+1 (ts i ) through the exponential function exp(-δ×GP A+1 (ts i )) constructs the inhibition term, and the working condition sensitivity factor δ controls the inhibition strength of grip on stability (the larger δ is, the greater the inhibition strength of grip decreases). The more significant the negative impact);

[0025] When GP A+1 (ts i ) decreases (such as when the road is slippery), the denominator increases, resulting in The significant decrease is consistent with the physical law of vehicle instability.

[0026] Preset ideal stability index BSP under normal conditions tar and stability deviation threshold τ, if Then determine the test scenario ts i If the stability is poor and there is a high-risk fault combination in the A+1th operating time period, an early warning will be issued to the relevant staff and the simulation detection strategy will be adjusted.

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

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

[0029] The database construction module is used to construct a failure mode database for a single operating time period corresponding to a test scenario;

[0030] The fault prediction module obtains a fault mode database for all operating time periods and predicts the probability of a pressure limiting valve seal failure fault, a wheel speed sensor drift fault, and a grip sensor abnormality fault occurring simultaneously in the next operating time period in the test scenario;

[0031] The parameter calculation and analysis warning module: if the test scenario simultaneously experiences a pressure limiting valve seal failure fault, a wheel speed sensor drift fault, and an abnormal grip perception fault in the next operating time period, the braking stability parameters of the test scenario in the next operating time period are calculated; a preset threshold is set, and an analysis is performed to issue a warning.

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

[0033] The data acquisition unit collects simulation operation data of the vehicle body electronic stability control system during historical simulation testing using data processing and analysis technology, the simulation operation data including pressure limiting valve sealing data, wheel speed sensor data, and grip perception data, and cleans and normalizes the pressure limiting valve sealing data, the wheel speed sensor data, and the grip perception data;

[0034] The set construction unit: constructs a test scenario set; evenly divides the simulation running time of each historical simulation detection 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 ath operating time period corresponding to the test scenario, specifically as follows: presetting a normal threshold value for pressure limiting valve sealing data; if the pressure limiting valve sealing data is less than the normal threshold value for pressure limiting valve sealing data, then determining that a pressure limiting valve sealing failure fault exists in the ath operating time period corresponding to the test scenario, and recording the result in the fault mode database; presetting a normal threshold range for wheel speed sensor data; if the wheel speed sensor data does not fall within the normal threshold range for wheel speed sensor data, then determining that a wheel speed sensor drift fault exists in the ath operating time period corresponding to the test scenario, and recording the result in the fault mode database; presetting a normal threshold range for grip perception data; if the grip perception data does not fall within the normal threshold range for grip perception data, then determining that an abnormal grip perception fault exists in the ath operating time period corresponding to the test scenario, and recording the result in the fault mode database.

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

[0038] The fault prediction unit obtains a fault mode database for all A operating time periods corresponding to the test scenario and constructs an observation data set; counts the number of simultaneous occurrences of a pressure limiting valve seal failure fault, a wheel speed sensor drift fault, and a grip perception abnormality fault, and predicts the probability of simultaneous occurrence of these faults in the A+1th operating time period of the test scenario.

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

[0040] The parameter calculation unit: a preset probability threshold, if the probability that the pressure limiting valve seal failure fault, the wheel speed sensor drift fault, and the grip perception abnormality fault will occur simultaneously in the test scenario in the A+1th running time period is greater than the probability threshold, then it is determined that the pressure limiting valve seal failure fault, the wheel speed sensor drift fault, and the grip perception abnormality fault will occur simultaneously in the test scenario in the A+1th running time period, and the braking stability parameter of the test scenario in the A+1th running time period is 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 is determined that the test scenario has poor stability in the A+1th operating time period and there is a high-risk fault combination. A warning is then issued to relevant personnel, and the simulation detection strategy is adjusted.

[0042] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in a simulation detection and analysis method and system for vehicle body electronic stability control provided by the present invention, by collecting and processing pressure limiting valve sealing data, wheel speed sensor data and grip perception data, a test scenario set is constructed, high-quality modeling and feature induction of historical simulation data are achieved, and a unified and reliable foundation is laid for subsequent analysis; by constructing a fault mode database of the test scenario in each operating time period, multi-dimensional feature fault identification and classification recording are achieved, and the accuracy and traceability of fault judgment are improved; 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, and quantitative analysis and early perception of fault trends are achieved, providing a scientific basis for risk prevention and control; when it is detected that the high-risk probability exceeds the threshold, the braking stability parameters are calculated and dynamically evaluated and intelligently warned in combination with the preset threshold, thereby achieving comprehensive monitoring of the electronic stability status of the vehicle body and adaptive adjustment of the simulation strategy, and ultimately significantly improving the accuracy, foresight and safety assurance capabilities of simulation detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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.

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

[0045] Figure 2 The present invention is a structural diagram of a simulation detection and analysis system for vehicle body electronic stability control. DETAILED DESCRIPTION

[0046] 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.

[0047] See also Figure 1 In the first embodiment, a simulation detection and analysis method for vehicle body electronic stability control is provided, the method comprising the following steps:

[0048] Step S1: Collecting the pressure limiting valve sealing data, wheel speed sensor data, and grip perception data of the vehicle body electronic stability control system during the historical simulation test process; and constructing a test scenario set.

[0049] Specifically, using data processing and analysis technology, simulated operation data of the vehicle electronic stability control system during historical simulation testing is collected, the simulated operation data including pressure limiting valve sealing data, wheel speed sensor data, and grip perception data, and the pressure limiting valve sealing data, the wheel speed sensor data, and the grip perception data are cleaned and normalized;

[0050] Construct a test scenario set, denoted as TS = {ts i |i∈[1,I]}, where ts i represents the i-th test scenario, and I represents the total number of test scenarios; the simulation running time of each historical simulation detection is evenly divided into several running time periods, and the running time period corresponding to each test scenario is obtained, where one test scenario corresponds to no less than one running time period.

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

[0052] Specifically, the i-th test scenario ts i The corresponding pressure limiting valve sealing data, wheel speed sensor data and grip perception data of the ath operating time period are recorded as PLV a (ts i ),WSS a (ts i ) and GP a (ts i );

[0053] Build test scenario ts i The corresponding failure mode database for the ath running time period is as follows:

[0054] Preset the normal threshold value θ of the pressure limiting valve sealing data, if PLV a (ts i )<θ, then determine the test scenario ts i The corresponding a-th operating time period has a pressure limiting valve seal failure fault, which is recorded in the fault mode database;

[0055] Preset wheel speed sensor data normal threshold range [α min ,α max ],like Then determine the test scenario ts i The corresponding a-th operating time period has a wheel speed sensor drift fault and is recorded in the fault mode database, where α min and α max Respectively represent the lower and upper limits of the normal threshold range of wheel speed sensor data;

[0056] Preset normal threshold range of grip perception data [βmin ,β max ],like Then determine the test scenario ts i The corresponding a-th operating time period has a grip perception abnormality fault and is recorded in the fault mode database, where β min and β max They represent the lower and upper limits of the normal threshold range of grip perception data respectively.

[0057] Step S3: Obtain the fault mode database for all operating time periods, and predict the probability of the test scenario having a pressure limiting valve seal failure fault, a wheel speed sensor drift fault, and a grip sensing abnormality fault simultaneously in the next operating time period.

[0058] Specifically, get the test scenario ts i The corresponding fault mode database of all A operating time periods is constructed, and an observation data set is constructed; the number of simultaneous occurrences of pressure limiting valve seal failure, wheel speed sensor drift failure, and grip perception abnormality failure is counted, and the test scenario ts is predicted. i The probability of the pressure limiting valve seal failure fault, wheel speed sensor drift fault, and grip sensor abnormality fault occurring simultaneously during the A+1th operating time period is as follows:

[0059] The test scenario ts i The pressure limiting valve seal failure fault, wheel speed sensor drift fault, and grip perception abnormality fault in the A+1th operating time period are recorded as F1, F2, and F3;

[0060] The observation data set 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 full operating time period, FA 1,a , FA 2,a and FA 3,a Represents the test scenario ts respectively i The corresponding pressure limiting valve seal failure fault, wheel speed sensor drift fault and grip perception abnormal fault in the ath operating time period, μ a represents the friction coefficient of the ath operating time period;

[0061] Calculate test scenario ts i The probability of the pressure limiting valve seal failure, wheel speed sensor drift failure, and grip sensor abnormality failure occurring simultaneously during the A+1th operating time period is calculated as follows:

[0062]

[0063] Among them, P(F1=1,F2=1,F3=1|ODS i ) represents the test scenario ts i The probability of simultaneous occurrence of pressure limiting valve seal failure, wheel speed sensor drift failure, and grip sensor abnormality failure during the A+1th operating time period, I(FA 1,a =1,FA 2,a =1,FA 3,a =1) represents the indicator function, if PLV a (ts i )<θ, then FA 1,a =1, if Then FA 2,a =1, if Then FA 3,a =1,ω a represents the friction weight factor, Indicates the preset attenuation factor.

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

[0065] Step S4: If the test scenario simultaneously experiences a pressure limiting valve seal failure fault, a wheel speed sensor drift fault, and a grip perception abnormality fault during the next operating time period, the braking stability parameters of the test scenario during the next operating time period are calculated; a threshold is preset, and an analysis is performed to issue an early warning.

[0066] Specifically, the probability threshold is preset. If the test scenario ts i The probability P(F1=1,F2=1,F3=1|ODS) that the pressure limiting valve seal failure fault, wheel speed sensor drift fault and grip sensor abnormality fault occur simultaneously in the A+1th operating time period i ) is greater than the probability threshold, then the test scenario ts is determined i In the A+1th operating time period, the pressure limiting valve seal failure, wheel speed sensor drift failure, and grip perception abnormality failure will occur simultaneously. Then the test scenario ts is calculated. i The braking stability parameter in the A+1th operating time period is calculated as follows:

[0067]

[0068] in, Represents the test scenario ts i Braking stability parameter in the A+1th operating time period, PLV A+1(ts i ),WSS A+1 (ts i ) and GP A+1 (ts i ) represent the test scenarios ts i The pressure limiting valve sealing data, wheel speed sensor data, and grip perception data for the A+1th operating time period. λ represents a preset fusion factor, which is used to adjust the nonlinear gain effect of the pressure limiting valve and wheel speed combination. δ represents a preset operating condition sensitivity factor, which is used to control the inhibitory effect of grip perception on the stability prediction value.

[0069] Preset ideal stability index BSP under normal conditions tar and stability deviation threshold τ, if Then determine the test scenario ts i If the stability is poor and there is a high-risk fault combination in the A+1th operating time period, an early warning will be issued to the relevant staff and the simulation detection strategy will be adjusted.

[0070] In the present invention, simply determining whether a fault mode exists cannot directly reflect the dynamic response of the vehicle under that operating condition, because the impact of the fault combination on the vehicle is not a simple linear superposition, and there may be coupling effects or amplification effects (for example, the pressure limiting valve and wheel speed drift will superimpose and amplify the instability risk at low friction). By determining the deviation of the braking stability parameter from the ideal stability index, a quantitative and safety boundary analysis of the impact of the previous fault combination is conducted, which is used to directly guide the control strategy of the simulation test (such as adjusting the braking force distribution and the ESC intervention strategy), forming a closed detection loop. In addition, first screening the fault mode through conditional probability and then conducting a detailed stability analysis of key operating conditions can reduce the computational burden and improve efficiency.

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

[0072] See also Figure 2 In the second embodiment, a simulation detection and analysis system for vehicle body electronic stability control is provided, the system comprising: a data acquisition and collection construction module, a database construction module, a fault prediction module, and a parameter calculation and analysis warning module;

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

[0074] The database construction module is used to construct a failure mode database for a single operating time period corresponding to a test scenario;

[0075] The fault prediction module obtains a fault mode database for all operating time periods and predicts the probability of a pressure limiting valve seal failure fault, a wheel speed sensor drift fault, and a grip sensor abnormality fault occurring simultaneously in the next operating time period in the test scenario;

[0076] The parameter calculation and analysis warning module: if the test scenario simultaneously experiences a pressure limiting valve seal failure fault, a wheel speed sensor drift fault, and an abnormal grip perception fault in the next operating time period, the braking stability parameters of the test scenario in the next operating time period are calculated; a preset threshold is set, and an analysis is performed to issue a warning.

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

[0078] The data acquisition unit collects simulation operation data of the vehicle body electronic stability control system during historical simulation testing using data processing and analysis technology, the simulation operation data including pressure limiting valve sealing data, wheel speed sensor data, and grip perception data, and cleans and normalizes the pressure limiting valve sealing data, the wheel speed sensor data, and the grip perception data;

[0079] The set construction unit: constructs a test scenario set; evenly divides the simulation running time of each historical simulation detection 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 ath operating time period corresponding to the test scenario, specifically as follows: presetting a normal threshold value for pressure limiting valve sealing data; if the pressure limiting valve sealing data is less than the normal threshold value for pressure limiting valve sealing data, then determining that a pressure limiting valve sealing failure fault exists in the ath operating time period corresponding to the test scenario, and recording the result in the fault mode database; presetting a normal threshold range for wheel speed sensor data; if the wheel speed sensor data does not fall within the normal threshold range for wheel speed sensor data, then determining that a wheel speed sensor drift fault exists in the ath operating time period corresponding to the test scenario, and recording the result in the fault mode database; presetting a normal threshold range for grip perception data; if the grip perception data does not fall within the normal threshold range for grip perception data, then determining that an abnormal grip perception fault exists in the ath operating time period corresponding to the test scenario, and recording the result in the fault mode database.

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

[0083] The fault prediction unit obtains a fault mode database for all A operating time periods corresponding to the test scenario and constructs an observation data set; counts the number of simultaneous occurrences of a pressure limiting valve seal failure fault, a wheel speed sensor drift fault, and a grip perception abnormality fault, and predicts the probability of simultaneous occurrence of these faults in the A+1th operating time period of the test scenario.

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

[0085] The parameter calculation unit: a preset probability threshold, if the probability that the pressure limiting valve seal failure fault, the wheel speed sensor drift fault, and the grip perception abnormality fault will occur simultaneously in the test scenario in the A+1th running time period is greater than the probability threshold, then it is determined that the pressure limiting valve seal failure fault, the wheel speed sensor drift fault, and the grip perception abnormality fault will occur simultaneously in the test scenario in the A+1th running time period, and the braking stability parameter of the test scenario in the A+1th running time period is 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 is determined that the test scenario has poor stability in the A+1th operating time period and there is a high-risk fault combination. A warning is then issued to relevant personnel, and the simulation detection strategy is adjusted.

[0087] In the third embodiment, a simulation detection and analysis method for vehicle body electronic stability control is provided. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0088] Assume that the total number of operating time periods A = 5, and the attenuation factor Friction coefficient μ a =[0.85, 0.82, 0.3, 0.8, 0.78], the pressure limiting valve seal failure fault, wheel speed sensor drift fault, and grip perception abnormality fault occurred simultaneously in the first and second operating time periods;

[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] The preset probability threshold is 0.4, then P(F1=1,F2=1,F3=1|ODS i ) is greater than the probability threshold, then calculate the test scenario ts i Braking stability parameters in the A+1th 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, working 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 in the A+1th operating time period, an early warning will be issued to the relevant staff and the simulation detection strategy will be adjusted.

[0100] 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.

[0101] 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. A simulation detection and analysis method for vehicle body electronic stability control, characterized in that: The method comprises the following steps: Step S1: collecting pressure limiting valve sealing data, wheel speed sensor data, and grip perception data of the vehicle body electronic stability control system during historical simulation testing; and constructing a test scenario set; Step S2: constructing a failure mode database for a single operating time period corresponding to the test scenario; Step S3: Obtaining a database of fault modes for all operating time periods, and predicting the probability of the pressure limiting valve seal failure, wheel speed sensor drift failure, and grip sensor abnormality failure occurring simultaneously in the next operating time period of the test scenario; Step S4: If the test scenario simultaneously experiences a pressure limiting valve seal failure fault, a wheel speed sensor drift fault, and a grip perception abnormality fault during the next operating time period, the braking stability parameters of the test scenario during the next operating time period are calculated; a threshold is preset, and an analysis is performed to issue an early warning.

2. The simulation detection and analysis method for vehicle body electronic stability control according to claim 1, characterized in that: The specific implementation process of step S1 includes: Using data processing and analysis technology, simulated operation data of the vehicle electronic stability control system during historical simulation testing is collected, the simulated operation data including pressure limiting valve sealing data, wheel speed sensor data, and grip perception data, and the pressure limiting valve sealing data, the wheel speed sensor data, and the grip perception data are cleaned and normalized; Construct a test scenario set, denoted as TS = {ts i |i∈[1,I]}, where ts i represents the i-th test scenario, and I represents the total number of test scenarios; the simulation running time of each historical simulation detection is evenly divided into several running time periods, and the running time period corresponding to each test scenario is obtained, where one test scenario corresponds to no less than one running time period.

3. The simulation detection and 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 scene ts i The corresponding pressure limiting valve sealing data, wheel speed sensor data and grip perception data of the ath operating time period are recorded as PLV a (ts i ),WSS a (ts i ) and GP a (ts i ); Build test scenario ts i The corresponding failure mode database for the ath running time period is as follows: Preset the normal threshold value θ of the pressure limiting valve sealing data, if PLV a (ts i )<θ, then determine the test scenario ts i The corresponding a-th operating time period has a pressure limiting valve seal failure fault, which is recorded in the fault mode database; Preset wheel speed sensor data normal threshold range [α min ,α max ],like Then determine the test scenario ts i The corresponding a-th operating time period has a wheel speed sensor drift fault and is recorded in the fault mode database, where α min and α max Respectively represent the lower and upper limits of the normal threshold range of wheel speed sensor data; Preset normal threshold range of grip perception data [β min ,β max ],like Then determine the test scenario ts i The corresponding a-th operating time period has a grip perception abnormality fault and is recorded in the fault mode database, where β min and β max They represent the lower and upper limits of the normal threshold range of grip perception data respectively.

4. The simulation detection and analysis method for vehicle body electronic stability control according to claim 3, characterized in that: The specific implementation process of step S3 includes: Get the test scenario ts i The corresponding fault mode database of all A operating time periods is constructed, and an observation data set is constructed; the number of simultaneous occurrences of pressure limiting valve seal failure, wheel speed sensor drift failure, and grip perception abnormality failure is counted, and the test scenario ts is predicted. i The probability of the pressure limiting valve seal failure fault, wheel speed sensor drift fault, and grip sensor abnormality fault occurring simultaneously during the A+1th operating time period is as follows: The test scenario ts i The pressure limiting valve seal failure fault, wheel speed sensor drift fault, and grip perception abnormality fault in the A+1th operating time period are recorded as F1, F2, and F3; The observation data set 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 full operating time period, FA 1,a , FA 2,a and FA 3,a Represents the test scenario ts respectively i The corresponding pressure limiting valve seal failure fault, wheel speed sensor drift fault and grip perception abnormal fault in the ath operating time period, μ a represents the friction coefficient of the ath operating time period; Calculate test scenario ts i The probability of the pressure limiting valve seal failure, wheel speed sensor drift failure, and grip sensor abnormality failure occurring simultaneously during the A+1th operating time period is calculated as follows: Among them, P(F1=1,F2=1,F3=1|ODS i ) represents the test scenario ts i The probability of simultaneous occurrence of pressure limiting valve seal failure, wheel speed sensor drift failure, and grip sensor abnormality failure during the A+1th operating time period, I(FA 1,a =1,FA 2,a =1,FA 3,a =1) represents the indicator function, if PLV a (ts i )<θ, then FA 1,a =1, if Then FA 2,a =1, if Then FA 3,a =1,ω a represents the friction weight factor, Indicates the preset attenuation factor.

5. The simulation detection and analysis method for vehicle body electronic stability control according to claim 4, characterized in that: The specific implementation process of step S4 includes: Preset probability threshold, if the test scenario ts i The probability P(F1=1,F2=1,F3=1|ODS) that the pressure limiting valve seal failure fault, wheel speed sensor drift fault and grip sensor abnormality fault occur simultaneously in the A+1th operating time period i ) is greater than the probability threshold, then the test scenario ts is determined i In the A+1th operating time period, the pressure limiting valve seal failure, wheel speed sensor drift failure, and grip perception abnormality failure will occur simultaneously. Then the test scenario ts is calculated. i The braking stability parameter in the A+1th operating time period is calculated as follows: in, Represents the test scenario ts i Braking stability parameter in the A+1th operating time period, PLV A+1 (ts i ),WSS A+1 (ts i ) and GP A+1 (ts i ) represent the test scenarios ts i The pressure limiting valve sealing data, wheel speed sensor data, and grip sensing data in the A+1th operating time period, where λ represents the preset fusion factor and δ represents the preset operating condition sensitivity factor; Preset ideal stability index BSP under normal conditions tar and stability deviation threshold τ, if Then determine the test scenario ts i If the stability is poor and there is a high-risk fault combination in the A+1th operating time period, an early warning will be issued to the relevant staff and the simulation detection strategy will be adjusted.

6. A simulation detection and analysis system for vehicle body electronic stability control, which executes a simulation detection and analysis method for vehicle body electronic stability control according to any one of claims 1 to 5, 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 and analysis warning module; The data acquisition and collection construction module collects pressure limiting valve sealing data, wheel speed sensor data and grip perception data of the vehicle body electronic stability control system during historical simulation testing; and constructs a test scenario collection; The database construction module is used to construct a failure mode database for a single operating time period corresponding to a test scenario; The fault prediction module obtains a fault mode database for all operating time periods and predicts the probability of a pressure limiting valve seal failure fault, a wheel speed sensor drift fault, and a grip sensor abnormality fault occurring simultaneously in the next operating time period in the test scenario; The parameter calculation and analysis warning module: if the test scenario simultaneously experiences a pressure limiting valve seal failure fault, a wheel speed sensor drift fault, and an abnormal grip perception fault in the next operating time period, the braking stability parameters of the test scenario in the next operating time period are calculated; a preset threshold is set, and an analysis is performed to issue a warning.

7. The simulation detection and analysis system for vehicle body electronic stability control 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 collects simulation operation data of the vehicle body electronic stability control system during historical simulation testing using data processing and analysis technology, the simulation operation data including pressure limiting valve sealing data, wheel speed sensor data, and grip perception data, and cleans and normalizes the pressure limiting valve sealing data, the wheel speed sensor data, and the grip perception data; The set construction unit is used to construct a test scenario set; 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 no less than one running time period.

8. The simulation detection and analysis system for vehicle body electronic stability control according to claim 7, characterized in that: The database construction module includes a database construction unit; The database construction unit is configured to construct a failure mode database for the ath operating time period corresponding to the test scenario, specifically as follows: a normal threshold value for the sealing data of the pressure limiting valve is preset; if the sealing data of the pressure limiting valve is less than the normal threshold value for the sealing data of the pressure limiting valve, it is determined that a pressure limiting valve sealing failure fault exists in the ath operating time period corresponding to the test scenario, and the fault is recorded in the failure mode database; A normal threshold range of 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 operating time period corresponding to the test scenario, and the fault mode database is recorded. A normal threshold range for grip perception data is preset. If the grip perception data does not fall within the normal threshold range for grip perception data, it is determined that an abnormal grip perception fault exists in the a-th operating time period corresponding to the test scenario, and the fault is recorded in the fault mode database.

9. The simulation detection and analysis system for vehicle body electronic stability control according to claim 8, characterized in that: The fault prediction module includes a fault prediction unit; The fault prediction unit: obtains a fault mode database of all A operating time periods corresponding to the test scenario and constructs an observation data set; Count the number of times the pressure limiting valve seal failure, wheel speed sensor drift failure, and grip perception abnormality failure occur simultaneously, and predict the probability of the pressure limiting valve seal failure, wheel speed sensor drift failure, and grip perception abnormality failure occurring simultaneously in the A+1th operating time period of the test scenario.

10. The simulation detection and analysis system for vehicle body electronic stability control according to claim 9, characterized in that: The parameter calculation and analysis warning module includes a parameter calculation unit and an analysis warning unit; The parameter calculation unit: a preset probability threshold, if the probability that the pressure limiting valve seal failure fault, the wheel speed sensor drift fault, and the grip perception abnormality fault will occur simultaneously in the test scenario in the A+1th running time period is greater than the probability threshold, then it is determined that the pressure limiting valve seal failure fault, the wheel speed sensor drift fault, and the grip perception abnormality fault will occur simultaneously in the test scenario in the A+1th running time period, and the braking stability parameter of the test scenario in the A+1th running time period is 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 is determined that the test scenario has poor stability in the A+1th operating time period and there is a high-risk fault combination. A warning is then issued to relevant personnel, and the simulation detection strategy is adjusted.

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