Fault diagnosis method and device for vehicle particle trap and vehicle

Abstract

The invention relates to the technical field of vehicle emission control, in particular to a fault diagnosis method and device for a vehicle particle trap, a vehicle and a storage medium, and the method comprises the steps: judging whether the vehicle meets a preset monitoring condition or not; if the vehicle meets the preset monitoring condition, a pressure difference sequence of the particulate filter is calculated, and whether the particulate filter meets a preset diagnosis condition or not is judged according to the pressure difference sequence; and if the particle trap meets the preset diagnosis condition, calculating a Pearson's correlation coefficient of the central point sequence and the pressure difference sequence of the particle trap, and generating a fault diagnosis result of the particle trap according to the Pearson's correlation coefficient. Therefore, the problems that in the related technology, when the differential pressure model is established, the accuracy of the differential pressure model is not high, and a single diagnosis strategy is difficult to meet the in-use monitoring frequency requirements of two vehicle types at the same time, so that the diagnosis efficiency is low, vehicle emission is affected, and the environment is polluted are solved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle emission control, and in particular to a fault diagnosis method for a vehicle particulate filter. Background Art

[0002] As global awareness of environmental protection increases, reducing particulate matter emissions in automobile exhaust has become an important issue that the automotive industry needs to solve urgently. In order to meet the requirements of relevant regulations on GPF (Gasoline Particulate Filter) performance diagnosis, that is, before the GPF performance declines and when the GPF is damaged or removed, OBD (On-Board Diagnostics) needs to accurately diagnose and warn. The existing technology mainly relies on monitoring the pressure difference before and after the GPF. Specifically, by installing a pressure difference sensor before and after the GPF, monitoring the pressure difference change, and combining the GPF pressure difference model in the engine control unit, fault judgment is performed.

[0003] In the related art, a GPF pressure difference model is usually established based on a large number of calibration tests. The difference or characteristic factor is calculated by comparing the pressure difference predicted by the model with the actual measured pressure difference. When the actual pressure difference is significantly lower than the pressure difference predicted by the model, it is judged that the GPF is removed or damaged. Alternatively, based on the GPF pressure difference model, the pressure difference change rate (gradient) is compared under vehicle dynamic conditions. If the actual pressure difference change rate is much smaller than the model prediction value, it is judged that the GPF is faulty.

[0004] However, in the related art, when establishing the GPF pressure difference model, there is an error (about ±5%) in the conversion of mass flow to volume flow, and the GPF temperature and pressure are simplified to a single center point value, resulting in inaccurate volume flow calculation, which affects the accuracy of the pressure difference model. In addition, the driving conditions of traditional fuel vehicles and hybrid vehicles are significantly different, and a single diagnostic strategy is difficult to meet the in-use monitoring frequency requirements of both models at the same time, resulting in low diagnostic efficiency. In summary, the related technology has certain limitations and needs to be improved urgently. Summary of the invention

[0005] The present application provides a vehicle particulate filter fault diagnosis method, device, vehicle and storage medium to solve the problems in the related art that when establishing a pressure difference model, the accuracy of the pressure difference model is not high, and a single diagnostic strategy is difficult to simultaneously meet the in-use monitoring frequency requirements of two vehicle models, resulting in low diagnostic efficiency, thereby affecting vehicle emissions and polluting the environment.

[0006] A first aspect of the present application provides a method for diagnosing a fault of a vehicle particulate filter, comprising the following steps: determining whether a vehicle satisfies a preset monitoring condition; if the vehicle satisfies the preset monitoring condition, calculating a pressure difference series of the particulate filter, and determining whether the particulate filter satisfies a preset diagnostic condition based on the pressure difference series; if the particulate filter satisfies the preset diagnostic condition, calculating a Pearson correlation coefficient between a center point series and a pressure difference series of the particulate filter, and generating a fault diagnosis result of the particulate filter based on the Pearson correlation coefficient.

[0007] Through the above-mentioned technical scheme, the embodiment of the present application can realize fault diagnosis of the particulate filter by introducing diagnostic conditions, pressure difference series analysis and calculation of the Pearson correlation coefficient when the vehicle meets certain monitoring conditions, effectively solve the problems of modeling errors and operating condition adaptability, improve the accuracy and efficiency of GPF fault diagnosis, and provide more reliable data support for the maintenance and management of automobile emission control systems.

[0008] Optionally, in one embodiment of the present application, the calculation of the pressure difference series of the particulate collector includes: splitting the volume flow range flowing through the particulate collector into equal parts to generate the center point series; obtaining the distance between the center point of the current operating exhaust flow of the particulate collector and the small area corresponding to the center point series, so as to obtain the pressure difference series in combination with the corresponding weighting coefficient.

[0009] Through the above technical solution, the embodiment of the present application can improve the calculation method of the pressure difference series through the refined analysis of the volume flow and the introduction of weighted coefficients, thereby improving the adaptability and accuracy of the diagnostic strategy to working conditions.

[0010] Optionally, in one embodiment of the present application, judging whether the particle collector satisfies a preset diagnostic condition based on the pressure difference series includes: obtaining the cumulative number of small cell center point distances falling within the target small cell to obtain a counter series; and when each cumulative number in the counter combing is greater than a preset number, determining that the particle collector satisfies the preset diagnostic condition.

[0011] Through the above technical solution, the embodiment of the present application can determine whether the vehicle meets the diagnostic conditions by comparing the cumulative number of cell center point distances falling within the target cell with a certain number, thereby optimizing the judgment process of whether the GPF meets the preset diagnostic conditions and improving the accuracy and robustness of fault diagnosis.

[0012] Optionally, in one embodiment of the present application, the preset monitoring conditions include at least one of the ambient temperature being greater than a preset temperature, the ambient pressure being greater than a preset pressure, the charging model being valid, the engine being started and lasting for a first preset time period, the oxygen sensor being over the dew point and lasting for a second preset time period, the engine not being in a catalyst heating condition or a fuel cut-off condition, the particulate filter not being in a regeneration condition, the differential pressure sensor being fault-free, and the volume flow rate through the particulate filter being within a preset volume flow rate range.

[0013] Through the above technical scheme, the monitoring conditions of the embodiment of the present application can include multiple constraints, so that the judgment result is more accurate, so as to improve the accuracy and reliability of GPF fault diagnosis, and provide scientific and rigorous starting conditions for GPF performance evaluation under complex working conditions.

[0014] A second aspect of the present application provides a fault diagnosis device for a vehicle particulate filter, comprising: a first judgment module, used to judge whether a vehicle satisfies a preset monitoring condition; a second judgment module, used to calculate a pressure difference series of a particulate filter when the vehicle satisfies the preset monitoring condition, and judge whether the particulate filter satisfies the preset diagnostic condition based on the pressure difference series; a generation module, used to calculate the Pearson correlation coefficient between the center point series and the pressure difference series of the particulate filter when the particulate filter satisfies the preset diagnostic condition, and generate a fault diagnosis result of the particulate filter based on the Pearson correlation coefficient.

[0015] Through the above-mentioned technical scheme, the embodiment of the present application can realize fault diagnosis of the particulate filter by introducing diagnostic conditions, pressure difference series analysis and calculation of the Pearson correlation coefficient when the vehicle meets certain monitoring conditions, effectively solve the problems of modeling errors and operating condition adaptability, improve the accuracy and efficiency of GPF fault diagnosis, and provide more reliable data support for the maintenance and management of automobile emission control systems.

[0016] Optionally, in one embodiment of the present application, the second judgment module includes: a first generating unit, used to split the volume flow range flowing through the particulate collector into equal parts to generate the center point series; a second generating unit, used to obtain the distance between the center points of the small intervals corresponding to the current operating exhaust flow of the particulate collector and the center point series, so as to obtain the pressure difference series in combination with the corresponding weighting coefficient.

[0017] Through the above technical solution, the embodiment of the present application can improve the calculation method of the pressure difference series through the refined analysis of the volume flow and the introduction of weighted coefficients, thereby improving the adaptability and accuracy of the diagnostic strategy to working conditions.

[0018] Optionally, in one embodiment of the present application, the second judgment module also includes: a third generation unit, used to obtain the cumulative number of small cell center point distances falling within the target small cell to obtain a counter series; a judgment unit, used to determine that the particle collector meets the preset diagnostic condition when each cumulative number in the counter combing is greater than a preset number.

[0019] Through the above technical solution, the embodiment of the present application can determine whether the vehicle meets the diagnostic conditions by comparing the cumulative number of cell center point distances falling within the target cell with a certain number, thereby optimizing the judgment process of whether the GPF meets the preset diagnostic conditions and improving the accuracy and robustness of fault diagnosis.

[0020] Optionally, in one embodiment of the present application, the preset monitoring conditions include at least one of the ambient temperature being greater than a preset temperature, the ambient pressure being greater than a preset pressure, the charging model being valid, the engine being started and lasting for a first preset time period, the oxygen sensor being over the dew point and lasting for a second preset time period, the engine not being in a catalyst heating condition or a fuel cut-off condition, the particulate filter not being in a regeneration condition, the differential pressure sensor being fault-free, and the volume flow rate through the particulate filter being within a preset volume flow rate range.

[0021] Through the above technical scheme, the monitoring conditions of the embodiment of the present application can include multiple constraints, so that the judgment result is more accurate, so as to improve the accuracy and reliability of GPF fault diagnosis, and provide scientific and rigorous starting conditions for GPF performance evaluation under complex working conditions.

[0022] A third aspect of the present application provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle particulate filter fault diagnosis method as described in the above embodiment.

[0023] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned vehicle particulate filter fault diagnosis method.

[0024] A fifth aspect of the present application provides a computer program, including a computer program, which, when executed, is used to implement the above-mentioned vehicle particulate filter fault diagnosis method.

[0025] The embodiments of the present application can introduce preset monitoring conditions (including but not limited to environmental conditions, system conditions, operating disturbances and flow conditions), pressure difference series analysis and Pearson correlation coefficient calculation, and improve the calculation method of the pressure difference series through refined analysis of volume flow and introduction of weighting coefficients. Furthermore, by comparing the counter series with the preset number, the judgment process of whether the GPF meets the preset diagnostic conditions is optimized, effectively solving the problems of modeling errors and operating adaptability, and improving the accuracy and efficiency of GPF fault diagnosis.

[0026] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 A flowchart of a fault diagnosis method for a vehicle particulate filter provided according to an embodiment of the present application;

[0029] Figure 2 is a function diagram of a weight function according to an embodiment of the present application;

[0030] Figure 3 is a flow chart of a fault diagnosis method for a vehicle particulate filter according to a specific embodiment of the present application;

[0031] Figure 4 A schematic diagram of the structure of a fault diagnosis device for a vehicle particulate filter provided according to an embodiment of the present application;

[0032] Figure 5 This is an example diagram of the structure of a vehicle provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0033] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0034] The following describes the fault diagnosis method, device, vehicle and storage medium of the vehicle particulate filter of the embodiment of the present application with reference to the accompanying drawings. In view of the related technology mentioned in the background technology center, when establishing the pressure difference model, the accuracy of the pressure difference model is not high, and a single diagnosis strategy is difficult to simultaneously meet the in-use monitoring frequency requirements of two vehicle models, resulting in low diagnostic efficiency, thereby affecting vehicle emissions and polluting the environment. The present application provides a fault diagnosis method for a vehicle particulate filter, in which the vehicle can be judged in real time whether it meets the monitoring conditions, and when the monitoring conditions are met, the pressure difference series of the GPF is calculated. After the pressure difference series is calculated, it is further judged whether the GPF meets the diagnostic conditions. This multi-level filtering mechanism effectively avoids invalid or immature diagnosis and ensures the reliability of the diagnostic results, thereby calculating the Pearson correlation coefficient of the center point series and the pressure difference series of the GPF, and generating a fault diagnosis result based on the judgment of the Pearson correlation coefficient, which not only improves the accuracy of fault diagnosis, but also can be optimized according to vehicle operating conditions and environmental conditions, and is suitable for a wider range of vehicle emission control system monitoring and diagnosis. Thereby, the problem in the related art that when establishing the pressure difference model, the accuracy of the pressure difference model is not high, and a single diagnostic strategy is difficult to meet the in-use monitoring frequency requirements of two types of vehicles at the same time, resulting in low diagnostic efficiency, thereby affecting vehicle emissions and polluting the environment.

[0035] Specifically, Figure 1 A flow chart of a vehicle particulate filter fault diagnosis method provided in an embodiment of the present application.

[0036] like Figure 1 As shown, the fault diagnosis method of the vehicle particulate filter includes the following steps:

[0037] In step S101, it is determined whether the vehicle meets the preset monitoring conditions.

[0038] It can be understood that the preset monitoring conditions are a preprocessing process for GPF removal diagnosis. When the preset monitoring conditions are met, the GPF removal diagnosis step is executed, wherein the preset monitoring conditions may include but are not limited to those obtained based on theoretical analysis or experimental data collection.

[0039] Optionally, in one embodiment of the present application, the preset monitoring conditions include at least one of the ambient temperature being greater than a preset temperature, the ambient pressure being greater than a preset pressure, the charging model being valid, the engine being started and lasting for a first preset time period, the oxygen sensor being above the dew point and lasting for a second preset time period, the engine not being in a catalyst heating condition or a fuel cut-off condition, the particulate filter not being in a regeneration condition, the differential pressure sensor being fault-free, and the volume flow rate through the particulate filter being within a preset volume flow rate range.

[0040] Further, the preset monitoring conditions are explained by taking an example:

[0041] The ambient temperature is greater than the preset temperature: the preset temperature is -7 degrees Celsius;

[0042] The ambient pressure is greater than the preset pressure: the preset pressure is 740 hPa;

[0043] The charging model is effective: usually refers to the engine's charging model being able to accurately calculate the actual air mass flow rate inhaled by the engine;

[0044] The engine starts and lasts for a first preset time: the engine starts and lasts for a period of time C1, C1 is a calibratable parameter, and the default value is 10s;

[0045] The oxygen sensor exceeds the dew point and lasts for a second preset time: The oxygen sensor exceeds the dew point and lasts for a period of time C2, C2 is a calibrable parameter, and the default value is 3s;

[0046] The engine is not in the catalyst heating condition or fuel cut-off condition: The catalyst heating condition requires additional fuel and oxygen, which may affect the engine's air-fuel ratio and the monitoring of the emission control system; the fuel cut-off condition is the state of intentionally reducing or completely cutting off the fuel supply. This may occur when the engine is overheated or the emission control system detects a fault. Therefore, these special conditions need to be excluded when performing performance diagnosis of the particulate filter;

[0047] The particulate filter is not in a regeneration condition: The high temperature and combustion activity during the regeneration process may affect the pressure and temperature readings of the particulate filter, thereby affecting the accuracy of the diagnostic results. Ensuring that the particulate filter is not under regeneration conditions can ensure that the data obtained is more reliable and help accurately determine the filtration efficiency and performance status of the particulate filter;

[0048] The differential pressure sensor is fault-free: The differential pressure sensor is a sensor used to measure the difference between two pressures. Its role in the particulate filter system is to monitor the pressure difference before and after the particulate filter. This pressure difference is crucial for diagnosing the blockage, regeneration efficiency and other potential performance problems of the particulate filter. The differential pressure sensor is fault-free, which means that the sensor can accurately measure the pressure difference and has no functional failure or measurement error. It can reliably provide data for the engine control unit to use to ensure the normal operation of the emission control system;

[0049] The volume flow rate flowing through the particulate filter is within the preset volume flow rate range: the volume flow rate flowing through the GPF is within the range [C3, C4), C3 and C4 are calibrable parameters, and the default values ​​are 150m3 / h and 1150m3 / h respectively.

[0050] If all monitoring conditions are met, the parameter Penabled = True, and the diagnostic data collection phase is ready to begin.

[0051] The embodiments of the present application can ensure that the subsequent GPF removal diagnosis is performed under the most suitable environment and working conditions through various detection conditions, thereby improving the accuracy of subsequent diagnosis.

[0052] In step S102, if the vehicle meets the preset monitoring conditions, the pressure difference series of the particulate trap is calculated, and it is determined whether the particulate trap meets the preset diagnosis conditions according to the pressure difference series.

[0053] It is understood that the pressure difference series refers to a series of data sets of the pressure difference before and after the particulate trap changing with time or working conditions under specific conditions, which is used to evaluate the blockage degree or working state of the particulate trap and is the key data for diagnosing the performance of the particulate trap. The preset diagnostic conditions are a series of set conditions used to determine whether the performance of the particulate trap meets the diagnostic standards, such as but not limited to the pressure difference threshold, blockage degree, etc.

[0054] Optionally, in one embodiment of the present application, the pressure difference series of the particulate collector is calculated, including: splitting the volume flow range flowing through the particulate collector into equal parts to generate a center point series; obtaining the distance between the center point of the small interval corresponding to the current operating exhaust flow of the particulate collector and the center point series, so as to obtain the pressure difference series in combination with the corresponding weighting coefficient.

[0055] Specifically, the volume flow range [C3, C4) flowing through the GPF is divided into equal parts, and the center points of each small interval form a sequence X, where X is a calibrable parameter with a default value of 200, 300, ..., 1200 m 3 / h, it can be divided into 10 small intervals with left closing and right opening, namely [150,250), [250,350), ..., [1050,1150).

[0056] Furthermore, the GPF pressure difference series Y is calculated by using a moving variable weighted filter. When Penabled = True, the exhaust flow is V and the GPF pressure difference is Dp. If V' falls within the i-th small interval for the first time, then Y i =Dp. If V' falls within the ith small interval again, then:

[0057] Y i =(1-C5*w(r))*Y i +C5*w(r)*Dp

[0058] Among them, Y i is the estimated pressure difference of GPF in the i-th exhaust flow interval, C5 is the basic moving weighting coefficient, which is a calibrable parameter with a default value of 0.5 and a value range of 0 to 1, w(r) is the weight function in the interval, and r is the distance between the exhaust flow of the current working condition and the center point of the small interval |V'-X i|. w(r) can be used to calibrate parameters. The default value is a Gaussian function, such as Figure 2 As shown by Figure 2 The value of w(r) corresponding to r can be found by searching the index. In the embodiment of the present application, a variety of functions can be selected as the weight function w(r), including but not limited to Gaussian function, exponential function, cubic spline and quartic spline, etc., and the characteristic is that w(r) decreases as the distance r increases.

[0059] Optionally, in one embodiment of the present application, whether the particle collector meets the preset diagnostic conditions is determined based on the pressure difference series, including: obtaining the cumulative number of small cell center point distances that fall within the target small cell to obtain a counter series; when each cumulative number in the counter combing is greater than the preset number, it is determined that the particle collector meets the preset diagnostic conditions.

[0060] In the actual execution process, when V' falls into a small interval, the counter in this small interval is accumulated by 1, and the counter is recorded as N, where N is a sequence with the same length as X. When each element of the counter N is greater than C6 (C6 is a calibrable parameter with a default value of 20), it is determined that enough data has been collected and the result can be judged, that is, Prelease = True.

[0061] The embodiment of the present application achieves a comprehensive, accurate and dynamic evaluation of the GPF performance status through refined volume flow arithmetic difference decomposition, efficient mobile variable weighted filtering calculation and counter-based diagnostic conditions.

[0062] In step S103, if the particulate trap meets the preset diagnosis condition, the Pearson correlation coefficient of the center point series and the pressure difference series of the particulate trap is calculated, and a fault diagnosis result of the particulate trap is generated according to the Pearson correlation coefficient.

[0063] It can be understood that the Pearson correlation coefficient is an indicator in statistics to measure the linear correlation between two variables, and its value range is between -1 and 1. In the embodiment of the present application, it is used to evaluate the relationship between the center point series and the pressure difference series, and to judge the stability or abnormality of the working state of the particle collector. In the embodiment of the present application, the description of the correlation between the center point series and the pressure difference series also includes a variety of algorithms such as the rank correlation coefficient, the kendall rank correlation coefficient, and the spearman correlation coefficient, which is characterized by the use of continuous data between -1 and 1 to characterize the correlation between the two variables.

[0064] Specifically, when Prelease=True, the Pearson correlation coefficient R of X and Y is calculated as follows:

[0065]

[0066] Where R is the Pearson correlation coefficient between X and Y, i is the element number of the exhaust flow series X and the GPF pressure difference series Y, is the average value of X, is the Y average value.

[0067] Furthermore, if R≤Rthd (Rthd is a calibratable parameter with a default value of 0.5), it is determined that a GPF removal fault has occurred; if R>Rthd, it is determined that there is no GPF removal fault. The numerator of IUPR (Incident Under Positive Response) increases by 1. It can be understood that IUPR is a counter in the fault diagnosis process, which is used to record and track the number of abnormal events detected in the GPF removal fault diagnosis to ensure the accuracy and reliability of the diagnosis results. After the diagnosis is completed, the counter series N and the GPF pressure difference series Y are cleared to 0, and the diagnosis steps are restarted.

[0068] The embodiment of the present application realizes the accurate identification and management of GPF faults through the use of statistical tools such as the Pearson correlation coefficient, combined with calibratable parameters and event counters, which not only improves the accuracy and efficiency of diagnosis, but also ensures the real-time and reliability of monitoring through a data reset mechanism.

[0069] The following combination Figure 3 Examples are listed for schematic illustration.

[0070] Step S301: Start.

[0071] Step S302: Determine whether the monitoring condition satisfies Penable == True.

[0072] If all monitoring conditions are met, the parameter Penabled=True, then step S303 is executed; if not, then step S301 is executed.

[0073] Step S303: Calculate the GPF pressure difference series Y by means of moving variable weighted filtering.

[0074] The moving variable weighted filtering method can not only accurately capture the dynamic changes of GPF pressure difference, but also effectively filter out noise and abnormal data, ensuring the accuracy and reliability of the pressure difference series Y, and providing solid data support for GPF performance evaluation and fault diagnosis.

[0075] Step S304: Determine whether the collected GPF pressure difference data Y meets the diagnosis condition Prelease==True.

[0076] If Prelease==True is satisfied, step S305 is executed; if not, step S301 is executed.

[0077] Step S305: Calculate the Pearson correlation coefficient R between X and Y.

[0078] The calculated result R ranges from -1 to 1. An R value close to 1 indicates that X and Y are highly positively correlated (i.e., their changing trends are consistent), an R value close to -1 indicates a high negative correlation (the changing trends are opposite), and an R value close to 0 indicates no significant linear correlation.

[0079] Step S306: The diagnosis result determines that R>Rthd.

[0080] If the diagnosis result satisfies R>Rthd, step S307 is executed; if not, step S308 is executed.

[0081] Step S307: The diagnosis is completed and there is no GPF removal failure, and the IUPR increases.

[0082] The increase in IUPR records the accumulation of the number of diagnoses under normal working conditions.

[0083] Step S308: The diagnosis is completed and there is a GPF removal fault, and the IUPR increases.

[0084] Based on the long-term record of IUPR, an early warning threshold can be set, and when the IUPR reaches a certain number of times, a preventive maintenance check is triggered.

[0085] Step S308: The counter sequence N and the GPF pressure difference sequence Y are cleared to 0.

[0086] It not only ensures the accuracy of monitoring and diagnosis, but also enables periodic self-update, providing a solid foundation for long-term performance monitoring and maintenance of GPF.

[0087] During the actual implementation process, when the vehicle engine is started, the embodiment of the present application enters a ready state and waits for the monitoring conditions to be met. Once the monitoring conditions are met, the GPF pressure difference data begins to be collected. These data reflect the performance of the GPF under real-time working conditions. The mobile variable weighted filtering technology is applied to perform real-time processing on the collected pressure difference data, effectively eliminating abnormal values ​​and noise, ensuring the accuracy and reliability of the pressure difference series Y, and providing a solid data foundation for fault diagnosis.

[0088] Furthermore, the embodiment of the present application further checks whether the collected data meets the diagnostic condition (Prelease==True), ensures that the data quantity and quality meet the diagnostic requirements, and uses the center point sequence X and the pressure difference sequence Y to calculate the Pearson correlation coefficient R between the two sequences, and judges the working state of the GPF by its value, providing a quantitative basis for subsequent fault diagnosis. The embodiment of the present application compares the calculated Pearson correlation coefficient R with the threshold Rthd to determine whether the GPF is in an abnormal state. If R>Rthd, it indicates that the GPF is working normally and there is no fault to be removed; otherwise, it indicates a possible fault. When the diagnosis result is normal, the IUPR counter increases and records the number of normal operation diagnosis. If a fault is detected, the IUPR also increases, and the fault handling process is triggered at the same time, including fault information recording and uploading to the cloud, providing data support for preventive maintenance. Finally, regardless of the diagnostic result, the embodiment of the present application will automatically clear the counter sequence N and the GPF pressure difference sequence Y to prepare for the next round of monitoring cycle, ensuring that each monitoring is based on the latest data, improving the accuracy of fault diagnosis and the real-time response.

[0089] According to the fault diagnosis method for a vehicle particulate filter proposed in an embodiment of the present application, it is possible to determine in real time whether the vehicle meets the monitoring conditions. If the monitoring conditions are met, the pressure difference series of the GPF is calculated. After calculating the pressure difference series, it is further determined whether the GPF meets the diagnostic conditions. This multi-level filtering mechanism effectively avoids invalid or immature diagnoses and ensures the reliability of the diagnostic results. The Pearson correlation coefficient of the center point series and the pressure difference series of the GPF is calculated, and based on the judgment of the Pearson correlation coefficient, a fault diagnosis result is generated. This not only improves the accuracy of fault diagnosis, but can also be optimized according to vehicle operating conditions and environmental conditions, and is suitable for a wider range of vehicle emission control system monitoring and diagnosis.

[0090] Next, the fault diagnosis device for the vehicle particulate filter according to the embodiment of the present application is described with reference to the accompanying drawings.

[0091] Figure 4 It is a block diagram of a fault diagnosis device for a vehicle particulate filter according to an embodiment of the present application.

[0092] like Figure 4 As shown, the vehicle particulate filter fault diagnosis device 10 includes: a first judgment module 100 , a second judgment module 200 and a generation module 300 .

[0093] Specifically, the first judgment module 100 is used to judge whether the vehicle meets the preset monitoring conditions.

[0094] The second judgment module 200 is used to calculate the pressure difference series of the particulate trap when the vehicle meets the preset monitoring conditions, and judge whether the particulate trap meets the preset diagnosis conditions according to the pressure difference series.

[0095] The generating module 300 is used to calculate the Pearson correlation coefficient between the center point series and the pressure difference series of the particulate trap when the particulate trap meets the preset diagnosis conditions, and generate a fault diagnosis result of the particulate trap according to the Pearson correlation coefficient.

[0096] Optionally, in one embodiment of the present application, the second determination module 200 includes: a first generation unit and a second generation unit.

[0097] The first generating unit is used to divide the volume flow range flowing through the particle collector into equal parts to generate a center point sequence.

[0098] The second generating unit is used to obtain the current working condition exhaust flow of the particulate trap and the distance between the center points of the small intervals corresponding to the center point series, so as to obtain the pressure difference series in combination with the corresponding weighting coefficient.

[0099] Optionally, in one embodiment of the present application, the second judgment module 200 further includes: a third generation unit and a judgment unit.

[0100] The third generating unit is used to obtain the cumulative number of cell center point distances falling within the target cell to obtain a counter sequence.

[0101] The determination unit is used to determine that the particle collector meets a preset diagnosis condition when each accumulated number in the counter combing is greater than a preset number.

[0102] Optionally, in one embodiment of the present application, the preset monitoring conditions include at least one of the ambient temperature being greater than a preset temperature, the ambient pressure being greater than a preset pressure, the charging model being valid, the engine being started and lasting for a first preset time period, the oxygen sensor being above the dew point and lasting for a second preset time period, the engine not being in a catalyst heating condition or a fuel cut-off condition, the particulate filter not being in a regeneration condition, the differential pressure sensor being fault-free, and the volume flow rate through the particulate filter being within a preset volume flow rate range.

[0103] It should be noted that the above explanations of the embodiment of the vehicle particulate filter fault diagnosis method are also applicable to the vehicle particulate filter fault diagnosis device of this embodiment, and will not be repeated here.

[0104] According to the fault diagnosis device for the vehicle particulate filter proposed in the embodiment of the present application, it is possible to determine in real time whether the vehicle meets the monitoring conditions. When the monitoring conditions are met, the pressure difference series of the GPF is calculated. After calculating the pressure difference series, it is further determined whether the GPF meets the diagnostic conditions. This multi-stage filtering mechanism effectively avoids invalid or immature diagnoses and ensures the reliability of the diagnostic results. The Pearson correlation coefficient of the center point series and the pressure difference series of the GPF is calculated, and the fault diagnosis result is generated based on the judgment of the Pearson correlation coefficient. This not only improves the accuracy of fault diagnosis, but also can be optimized according to vehicle operating conditions and environmental conditions, and is suitable for a wider range of vehicle emission control system monitoring and diagnosis.

[0105] Figure 5 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0106] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0107] When the processor 502 executes the program, the fault diagnosis method for the vehicle particulate filter provided in the above embodiment is implemented.

[0108] Furthermore, the vehicle also includes:

[0109] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0110] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0111] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0112] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0113] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0114] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0115] The embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned vehicle particulate filter fault diagnosis method.

[0116] The embodiment of the present application also provides a computer program product, including a computer program, which is used to implement the above-mentioned vehicle particulate filter fault diagnosis method when the computer program is executed.

[0117] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0118] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0119] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0120] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0121] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0122] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0123] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0124] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A fault diagnosis method for a vehicle particulate filter, characterized in that: The following steps are involved: Determine whether the vehicle meets the preset monitoring conditions; If the vehicle meets the preset monitoring condition, calculating a pressure difference series of the particulate trap, and judging whether the particulate trap meets the preset diagnosis condition according to the pressure difference series; If the particulate trap meets the preset diagnosis condition, the Pearson correlation coefficient of the center point series and the pressure difference series of the particulate trap is calculated, and a fault diagnosis result of the particulate trap is generated according to the Pearson correlation coefficient.

2. The method according to claim 1, characterized in that The step of calculating the pressure difference series of the particle trap includes: Splitting the volume flow range flowing through the particle collector arithmetic difference to generate the center point sequence; The current working condition exhaust flow of the particulate trap and the distance between the center points of the small intervals corresponding to the center point series are obtained, so as to obtain the pressure difference series in combination with the corresponding weighting coefficient.

3. The method according to claim 2, characterized in that The step of judging whether the particulate trap meets a preset diagnosis condition according to the pressure difference series includes: Obtain the cumulative number of the distances between the center points of the cell intervals falling within the target cell interval to obtain a counter sequence; When each accumulated number in the counter combing is greater than a preset number, it is determined that the particle trap meets the preset diagnosis condition.

4. The method according to claim 1, characterized in that: The preset monitoring conditions include at least one of the ambient temperature being greater than the preset temperature, the ambient pressure being greater than the preset pressure, the charging model being valid, the engine being started and lasting for a first preset time period, the oxygen sensor being above the dew point and lasting for a second preset time period, the engine not being in a catalyst heating condition or a fuel cut-off condition, the particulate filter not being in a regeneration condition, the differential pressure sensor being fault-free, and the volume flow rate through the particulate filter being within a preset volume flow rate range.

5. A control device for preventing a vehicle throttle from freezing, characterized in that: include: A first judgment module is used to judge whether the vehicle meets the preset monitoring conditions; A second judgment module is used to calculate a pressure difference series of a particulate trap when the vehicle meets the preset monitoring condition, and judge whether the particulate trap meets the preset diagnosis condition according to the pressure difference series; A generating module is used to calculate the Pearson correlation coefficient of the center point series and the pressure difference series of the particulate trap when the particulate trap meets the preset diagnostic condition, and generate a fault diagnosis result of the particulate trap according to the Pearson correlation coefficient.

6. The device according to claim 5, characterized in that The second judgment module includes: A first generating unit, configured to divide the volume flow range flowing through the particle collector into equal parts to generate the center point sequence; The second generating unit is used to obtain the current working condition exhaust flow of the particulate trap and the distance between the center points of the small cells corresponding to the center point series, so as to obtain the pressure difference series in combination with the corresponding weighting coefficient.

7. The device according to claim 5, characterized in that The second judgment module also includes: The third generating unit is used to obtain the cumulative number of the center point distances between the cells falling within the target cell to obtain a counter sequence; The determination unit is used to determine that the particle trap meets the preset diagnosis condition when each accumulated number in the counter combing is greater than a preset number.

8. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the fault diagnosis method for a vehicle particulate filter according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the fault diagnosis method for a vehicle particulate filter according to any one of claims 1 to 4.

10. A computer program product, characterized in that The invention comprises a computer program, wherein the computer program is executed to implement the fault diagnosis method of the vehicle particulate filter according to any one of claims 1 to 4.

Images