A method, apparatus, equipment and storage medium for fault diagnosis of a particulate filter.

Abstract

This invention provides a method, apparatus, device, and storage medium for diagnosing particulate filters. The method includes: acquiring engine operating conditions; when the operating conditions meet the fault diagnosis conditions of the particulate filter, acquiring the engine's exhaust volumetric flow rate and the upstream and downstream pressure difference of the particulate filter; normalizing the exhaust volumetric flow rate and the upstream and downstream pressure difference, and calculating the angle between the line connecting the normalized data point and the origin in the Cartesian coordinate system and the X-axis; when the angle is less than a first threshold, determining that the particulate filter has a removal fault; wherein, the X-axis of the Cartesian coordinate system corresponds to the exhaust volumetric flow rate, and the Y-axis corresponds to the upstream and downstream pressure difference. This technical solution improves the versatility and accuracy of the particulate filter fault diagnosis strategy and solves the problem of inconsistent diagnostic strategies for traditional fuel vehicles and hybrid vehicles.

Description

Technical Field

[0001] This invention relates to the field of motor vehicle particulate matter traps, and in particular to a fault diagnosis method, apparatus, and storage medium for particulate matter traps. Background Technology

[0002] A particulate filter is a device installed in the exhaust system of a car engine. It is a type of ceramic filter whose main function is to filter fine particulate matter in engine exhaust. For gasoline engines, when the particulate matter accumulated in the Gasoline Particulate Filter (GPF) reaches a certain level, the burner will automatically ignite and burn it, turning the adsorbed carbon soot particles into carbon dioxide and emitting it, thus optimizing the engine's performance.

[0003] Current GPF diagnostic systems operate by installing differential pressure sensors. They build a model based on the differential pressure-flow characteristics of the GPF. Under steady-state conditions, if the actual differential pressure is significantly lower than the model prediction or a set threshold, the GPF is considered removed. Under dynamic conditions, if the measured differential pressure gradient is much smaller than the model gradient, the GPF is also considered missing. However, this process simplifies the estimation of the GPF's temperature and pressure fields, leading to large calculation errors. The actual differential pressure and volumetric flow rate are only correlated, not precisely correlated.

[0004] Furthermore, traditional gasoline vehicles operate under more dynamic conditions and less steady-state conditions during driving, while hybrid vehicles are the opposite. If the same diagnostic strategy is used for both the gasoline and hybrid versions of the same model, one of the models may struggle to meet the regulatory requirements for in-use monitoring frequency. If different diagnostic strategies are used, the calibration data for the GPF diagnostics in the engine control unit must be managed separately, causing inconvenience. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and storage medium for diagnosing particulate filters, which improves the versatility and accuracy of particulate filter fault diagnosis strategies and solves the problem of difficulty in unifying diagnostic strategies for traditional fuel vehicles and hybrid vehicles.

[0006] According to one aspect of the present invention, an embodiment of the present invention provides a fault diagnosis method for a particle trap, comprising:

[0007] Obtain the engine's operating conditions;

[0008] When the operating conditions meet the fault diagnosis conditions of the particulate filter, obtain the exhaust volume flow rate of the engine and the pressure difference between the upstream and downstream of the particulate filter.

[0009] The exhaust volume flow rate and upstream and downstream pressure difference are normalized, and the angle between the line connecting the normalized data point and the origin in the Cartesian coordinate system and the X-axis is calculated.

[0010] When the included angle is less than the first threshold, it is determined that the particle collector has a removal fault;

[0011] In the Cartesian coordinate system, the X-axis corresponds to the exhaust volume flow rate, and the Y-axis corresponds to the upstream and downstream pressure difference.

[0012] Optionally, obtain the engine's operating conditions, including:

[0013] Obtain the ambient temperature and pressure during engine operation;

[0014] Is the engine inflation model valid?

[0015] Acquire the duration of engine start-up and the oxygen sensor over-dew point duration;

[0016] To determine whether the engine is in catalytic converter heating mode;

[0017] Determine if the engine is in a fuel cut-off condition;

[0018] Check if the particle collector is in regeneration mode;

[0019] Check if the differential pressure sensor of the particle trap is faulty;

[0020] Obtain the volumetric flow rate range through the particulate filter.

[0021] Optionally, the fault diagnosis conditions of the particle trap shall be met, including:

[0022] The ambient temperature is greater than -7℃;

[0023] Environmental pressure greater than 740 hPa;

[0024] The inflatable model is effective;

[0025] The engine starts and continues for the first preset time;

[0026] The oxygen sensor exceeds the dew point and remains in operation for a second preset time.

[0027] The engine is not in catalytic converter heating mode;

[0028] The engine is not in a fuel cut-off condition;

[0029] The particulate filter is not in regeneration mode;

[0030] The differential pressure sensor of the particulate filter is functioning correctly.

[0031] The volumetric flow rate passing through the particle collector is within a first preset range.

[0032] Optionally, before normalizing the exhaust volume flow rate and upstream / downstream pressure difference, the following steps are also included:

[0033] The normalization coefficients are calibrated using the median of the particle trap. The calibration process for the normalization coefficients includes:

[0034] Record the upstream and downstream pressure differences P1 and P2 when the exhaust volume flow rates are V1 and V2, respectively, and then the normalization coefficient is...

[0035] Optionally, the exhaust volume flow rate and upstream / downstream pressure difference can be normalized, including:

[0036] Given the exhaust volumetric flow rate V3 and the upstream and downstream pressure difference Dp1, the normalized exhaust volumetric flow rate is... Normalized upstream and downstream pressure difference

[0037] Calculate the angle between the line connecting the normalized data points and the origin in the Cartesian coordinate system and the X-axis, including:

[0038] According to the formula for calculating the included angle Calculate the included angle.

[0039] Optionally, when the operating conditions meet the fault diagnosis conditions of the particle trap, the following may also be included:

[0040] The exhaust volume flow rate of the engine and the pressure difference between the upstream and downstream of the particulate filter were obtained multiple times.

[0041] Based on multiple sets of exhaust volume flow rate and upstream and downstream pressure difference data, the moving average filter value θ of the included angle is calculated. filter ;

[0042] According to θ filter Determine if the particle trap has a removal fault.

[0043] Optionally, the moving average filter value θ of the included angle can be calculated based on multiple sets of exhaust volume flow rate and upstream and downstream pressure difference data. filter ,include:

[0044] When the fault diagnosis conditions of the particle trap are met for the first time, n = 1, θ filter (n) = θ; when the fault diagnosis condition of the particle trap is met for the nth time, n>1, θ filter (n)=(1-C2)×θ filter (n-1)+C2×θ, where C2 is a calibrable constant parameter;

[0045] According to θ filter Determining if the particle trap has a removal fault includes:

[0046] When n > C3, fault diagnosis is performed;

[0047] If θ at this timefilter (n) ≥ C4, it is determined that the diagnosis is completed and there is no fault in the removal of the particulate filter;

[0048] If θ at this time filter (n) < C4, it is determined that the diagnosis is completed and there is a fault in the removal of the particulate filter;

[0049] Both C3 and C4 are calibratable constant parameters.

[0050] According to another aspect of the present invention, an embodiment of the present invention provides a fault diagnosis device for a particulate filter, including:

[0051] An operating condition acquisition module, configured to acquire the operating conditions of the engine;

[0052] A data acquisition module, configured to acquire the exhaust gas volume flow rate of the engine and the upstream and downstream pressure difference of the particulate filter when the operating conditions meet the fault diagnosis conditions of the particulate filter;

[0053] A calculation module, which normalizes the exhaust gas volume flow rate and the upstream and downstream pressure difference, and calculates the angle between the line connecting the data point in the Cartesian coordinate system after normalization and the origin and the X-axis;

[0054] A fault determination module, configured to determine that there is a fault in the removal of the particulate filter.

[0055] According to another aspect of the present invention, an embodiment of the present invention provides a fault diagnosis device for a particulate filter, including:

[0056] One or more processors;

[0057] A memory, configured to store one or more programs;

[0058] When one or more programs are executed by one or more processors, one or more processors implement the fault diagnosis method for a particulate filter provided by any embodiment of the present invention.

[0059] According to another aspect of the present invention, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the fault diagnosis method for a particulate filter provided by any embodiment of the present invention.

[0060] The particulate filter fault diagnosis method provided in this invention obtains the engine's operating conditions; when the operating conditions meet the fault diagnosis conditions of the particulate filter, it obtains the engine's exhaust volumetric flow rate and the upstream and downstream pressure difference of the particulate filter; it normalizes the exhaust volumetric flow rate and the upstream and downstream pressure difference, and calculates the angle between the line connecting the normalized data point and the origin in the Cartesian coordinate system and the X-axis; when the angle is less than a first threshold, it is determined that the particulate filter has a removal fault; wherein, the X-axis of the Cartesian coordinate system corresponds to the exhaust volumetric flow rate, and the Y-axis corresponds to the upstream and downstream pressure difference. This invention proposes an innovative particulate filter fault diagnosis method that does not rely on complex pressure difference model calibration, nor does it require setting different monitoring frequencies for fuel-powered and hybrid vehicles, thereby significantly improving the universality and applicability of the fault diagnosis strategy. This improvement effectively solves the problem of difficulty in unifying the diagnostic strategies for traditional fuel-powered vehicles and hybrid vehicles, ensuring that both can meet the regulatory requirements for in-use monitoring frequencies.

[0061] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily apparent from the following description. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in this embodiment, the accompanying drawings used in the description of the embodiment will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this embodiment. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 A flowchart of a fault diagnosis method for a particle trap provided in an embodiment of the present invention;

[0064] Figure 2 A flowchart of another fault diagnosis method for a particle trap provided in an embodiment of the present invention;

[0065] Figure 3 A schematic diagram of normal and GPF failure characteristics of a particle trap provided in an embodiment of the present invention;

[0066] Figure 4 A flowchart of another fault diagnosis method for a particle trap provided in an embodiment of the present invention;

[0067] Figure 5 A flowchart of another fault diagnosis method for a particle trap provided in an embodiment of the present invention;

[0068] Figure 6 This is a structural block diagram of a fault diagnosis device for a particle trap provided in an embodiment of the present invention;

[0069] Figure 7 This is a structural schematic diagram of the fault diagnosis device for the particle trap provided in an embodiment of the present invention. Detailed Implementation

[0070] To enable those skilled in the art to better understand this solution, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments, and not all of the embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0071] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0072] Figure 1 This is a flowchart illustrating a fault diagnosis method for a particulate trap provided in an embodiment of the present invention. This embodiment is applicable when the operating conditions meet the fault diagnosis conditions of the particulate trap, determining whether there is a need to remove the particulate trap. This method can be executed by the particulate trap fault diagnosis device provided in this embodiment of the present invention. This particulate trap fault diagnosis device can be implemented in hardware and / or software, and can be configured in the particulate trap fault diagnosis equipment provided in this embodiment of the present invention. Figure 1 As shown, the method includes:

[0073] S110, Obtain the engine's operating conditions.

[0074] The engine operating conditions can be understood as a comprehensive management of the engine's operating conditions, including its operating status, performance, fuel economy, intake and emission levels.

[0075] Specifically, before checking whether the particulate filter is faulty, it is necessary to first determine whether the components related to the particulate filter in the vehicle are operating normally, which mainly includes the engine and its operating status, and the particulate filter and its operating status.

[0076] In an optional embodiment, obtaining the engine's operating conditions includes:

[0077] Obtain the ambient temperature and pressure during engine operation;

[0078] Is the engine inflation model valid?

[0079] Acquire the duration of engine start-up and the oxygen sensor over-dew point duration;

[0080] To determine whether the engine is in catalytic converter heating mode;

[0081] Determine if the engine is in a fuel cut-off condition;

[0082] Check if the particle collector is in regeneration mode;

[0083] Check if the differential pressure sensor of the particle trap is faulty;

[0084] Obtain the volumetric flow rate range through the particulate filter.

[0085] Here, the ambient temperature during engine operation can be understood as the atmospheric temperature of the external environment; the ambient pressure during engine operation can be understood as the atmospheric pressure of the external environment; the engine charging model can be understood as a model describing the engine charging process, the main function of which is to accurately calculate the charging mass in the combustion chamber, thereby determining the required fuel injection quantity; obtaining the duration after engine start can be understood as obtaining the time elapsed from engine start to the current moment through a timer; the oxygen sensor dew point duration can be understood as the time elapsed from when the oxygen sensor reaches the dew point temperature to the current moment, obtained through a timer. Point temperature refers to the critical temperature at which water vapor in the exhaust system will no longer condense; catalytic converter heating mode can be understood as determining whether the current water temperature is within the range required for catalytic converter heating by reading data from the engine coolant temperature sensor; fuel cut-off mode can be understood as the working mode in which the engine temporarily cuts off the fuel supply under specific conditions; regeneration mode can be understood as an operating mode in which the GPF is restored to its initial state by burning particles in the GPF; pressure difference of the particulate filter can be understood as the pressure difference between the inlet and outlet of the GPF; volumetric flow rate through the particulate filter can be understood as the volume of gas passing through the particulate filter per unit time.

[0086] Specifically, before checking for a malfunction in the particulate filter (GPF), it's necessary to first ensure that all components related to the GPF in the vehicle are functioning properly. The following conditions help determine if the engine is operating normally, if exhaust emissions are normal, and if the GPF is functioning correctly: Ambient temperature and pressure during engine operation affect engine performance and exhaust emission characteristics. For example, low temperatures can make GPF regeneration difficult because regeneration requires higher temperatures to burn accumulated particulate matter. Similarly, changes in ambient pressure can affect exhaust gas flow velocity and GPF collection efficiency. The effectiveness of the engine's charging model directly impacts intake efficiency and power performance. A malfunctioning charging model can lead to insufficient air intake, affecting combustion efficiency and exhaust emissions. The duration of engine start-up and the oxygen sensor's over-dew point duration reflect engine warm-up and stabilization. The oxygen sensor's over-dew point duration is related to the oxygen content and emission quality in the exhaust gas. The engine's catalytic converter heating conditions affect the exhaust preheating process. If the catalytic converter fails to heat properly, its conversion efficiency may be affected, leading to excessive exhaust emissions. Meanwhile, the catalytic converter's heating process can also affect the GPF's regeneration temperature and efficiency. Under engine fuel cut-off conditions, the engine temporarily cuts off the fuel supply. This leads to reduced exhaust emissions, and because regeneration requires fuel combustion to generate high-temperature particulate matter, the GPF cannot regenerate under fuel cut-off conditions. Regeneration is a crucial process for the GPF to remove accumulated particulate matter; during regeneration, it's impossible to determine if the particulate filter is malfunctioning. The differential pressure sensor is a key component for monitoring the degree of GPF clogging. If the sensor malfunctions, it will not accurately reflect the GPF's clogging status, thus affecting the particulate filter's fault diagnosis. The volumetric flow rate range reflects the amount of exhaust gas passing through the GPF, directly affecting the GPF's collection efficiency and regeneration requirements. If the volumetric flow rate is too high, it may cause rapid GPF clogging; conversely, if the volumetric flow rate is too low, it may affect the GPF's normal collection and regeneration efficiency.

[0087] S120. When the operating conditions meet the fault diagnosis conditions of the particulate filter, obtain the exhaust volume flow rate of the engine and the pressure difference between the upstream and downstream of the particulate filter.

[0088] Among them, the fault diagnosis conditions of the particulate filter can be understood as: the upstream and downstream pressure difference of the particulate filter can be understood as the air or gas pressure difference between the inlet (upstream) and outlet (downstream) of the particulate filter obtained by the pressure difference sensor of the particulate filter.

[0089] Specifically, when the operating conditions meet the fault diagnosis conditions of the particulate filter, it indicates that other factors that may affect the normal operation of the particulate filter have been eliminated, and the fault diagnosis process of the particulate filter will be formally entered. By obtaining the exhaust volume flow rate of the engine and the pressure difference between the upstream and downstream of the particulate filter, it is determined whether there is a removal fault in the particulate filter.

[0090] In an optional embodiment, satisfying the fault diagnosis conditions of the particle trap includes:

[0091] The ambient temperature is greater than -7℃;

[0092] Environmental pressure greater than 740 hPa;

[0093] The inflatable model is effective;

[0094] The engine starts and continues for the first preset time;

[0095] The oxygen sensor exceeds the dew point and remains in operation for a second preset time.

[0096] The engine is not in catalytic converter heating mode;

[0097] The engine is not in a fuel cut-off condition;

[0098] The particulate filter is not in regeneration mode;

[0099] The differential pressure sensor of the particulate filter is functioning correctly.

[0100] The volumetric flow rate passing through the particle collector is within a first preset range.

[0101] The first preset time can be understood as the minimum time preset in the particulate filter's fault diagnosis device to ensure that the engine has been running stably and has accumulated enough particulate matter; the second preset time can be understood as the time judgment node preset in the particulate filter's fault diagnosis device to ensure that the water vapor in the engine exhaust has condensed and is running stably; the first preset range can be understood as the flow rate preset in the particulate filter's fault diagnosis device to determine that the volumetric flow rate flowing through the particulate filter is within a flow rate range that can ensure the accuracy of the diagnostic results.

[0102] For example, the engine starts and continues for a first preset time of T1, where T1 is a calibrable parameter with a default value of 10s; the oxygen sensor passes the dew point and continues for a second preset time of T2, where T2 is a calibrable parameter with a default value of 3s; the volumetric flow rate through the particulate filter is within a first preset range of [T3, T4], where T3 and T4 are calibrable parameters with default values ​​of 500m³ and 500m³ respectively. 3 / h, 1500m 3 / h.

[0103] Specifically, an ambient temperature greater than -7℃ and an ambient pressure greater than 740hPa indicate that the particulate filter is operating at normal temperature and pressure; an effective charging model indicates normal engine intake; engine starting and remaining so for a first preset time, oxygen sensor exceeding dew point and remaining so for a second preset time, engine not in catalytic converter heating mode, and engine not in fuel cut-off mode indicate normal engine exhaust and stable operation; the particulate filter not in regeneration mode, the particulate filter differential pressure sensor being fault-free, and the volumetric flow rate through the particulate filter being within a first preset range indicate normal particulate filter operation. These fault diagnosis conditions for the particulate filter are determined comprehensively from multiple perspectives, including the working principle of the particulate filter, fault diagnosis requirements, and the influence of environmental factors. Failure to meet these conditions does not necessarily mean the particulate filter is not working properly, but it may experience performance degradation. This could prevent the particulate filter's self-diagnosis process from determining whether the fault is due to its own damage or external factors. Therefore, the particulate filter's fault diagnosis process will only run when the above conditions are met.

[0104] S130. Normalize the exhaust volume flow rate and the upstream and downstream pressure difference, and calculate the angle between the line connecting the normalized data point and the origin in the Cartesian coordinate system and the X-axis.

[0105] Specifically, let the exhaust volumetric flow rate be denoted as V3, the GPF pressure difference as Dp1, and the exhaust volumetric flow rate normalization coefficient as C. v The normalized coefficient of pressure difference is denoted as C. p Establish a Cartesian coordinate system with (0,0) as the origin, x as the horizontal axis, and y as the vertical axis. The horizontal axis represents the volumetric flow rate V3, and the vertical axis represents the GPF pressure difference Dp1. When the fault diagnosis conditions of the particulate filter are met, the normalized exhaust flow rate V4 = V3 / C v Normalized GPF pressure difference Dp2 = Dp1 / C p The angle between the line connecting a single data point to the origin and the X-axis is θ = arctan(Dp² / V⁴). In this formula, arctan() is the arctangent function.

[0106] S140. When the included angle is less than the first threshold, it is determined that the particle collector has a removal fault.

[0107] The first threshold can be understood as the minimum value of the angle between a single data point and the X-axis in the Cartesian coordinate system, preset in the fault diagnosis device of the particle trap. For example, the first threshold can be 10°.

[0108] Specifically, by judging the relationship between the included angle and the first threshold, when the included angle is greater than the first threshold, it can be judged that the GPF is in normal working condition. When the included angle is less than the first threshold, it can be judged that the GPF has a removal fault, that is, the GPF has been removed or damaged and has failed to work normally.

[0109] This embodiment first ensures that key components related to the particulate filter (such as the engine) are in normal working condition by acquiring the engine's operating conditions before checking for particulate filter malfunctions. Then, when the operating conditions meet the fault diagnosis criteria for the particulate filter, the engine's exhaust volume flow rate and the upstream and downstream pressure difference of the GPF are acquired to further confirm whether the GPF is in a diagnosable state, and key data for determining whether a removal fault exists in the GPF is obtained. Subsequently, the exhaust volume flow rate and upstream and downstream pressure difference are normalized, and the angle between the line connecting the normalized data point and the origin in the Cartesian coordinate system and the X-axis is calculated. This angle reflects the GPF's operating status. Finally, the angle is used to determine whether a removal fault exists in the GPF. If the angle is less than a preset first threshold, it can be considered that the GPF has been removed or is damaged, requiring corresponding maintenance measures. This invention provides a comprehensive, accurate, and efficient method for diagnosing particulate filter (GPF) faults. By comprehensively considering engine operating conditions and GPF working status, and employing normalization and angle calculation methods, it achieves accurate diagnosis and fault warning of GPF performance, thereby improving the accuracy and reliability of particulate filter fault diagnosis, reducing particulate filter maintenance time and costs, and enhancing the overall performance and reliability of the particulate filter.

[0110] Figure 2 A flowchart of another fault diagnosis method for a particle trap provided in an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the normal and fault characteristics of a particulate filter (GPF) according to an embodiment of the present invention. Based on the above embodiments, this embodiment explains how to calibrate the normalization coefficient using the center-value element of the particulate filter before normalizing the exhaust volume flow rate and upstream / downstream pressure difference. For example... Figure 2 As shown, the method includes:

[0111] S210, Obtain the engine's operating conditions.

[0112] S220. When the operating conditions meet the fault diagnosis conditions of the particulate filter, obtain the exhaust volume flow rate of the engine and the pressure difference between the upstream and downstream of the particulate filter.

[0113] S221. Calibrate the normalization coefficient using the median element of the particulate filter. The calibration process of the normalization coefficient includes: recording the upstream and downstream pressure differences P1 and P2 corresponding to exhaust volume flow rates V1 and V2, respectively. Then, the normalization coefficient...

[0114] Specifically, by selecting two flow points V1 and V2 within a first preset range of volumetric flow rate through the particulate filter, these two flow points should represent typical flow conditions within this preset range. For example, V1 and V2 can be the two endpoints of the first preset range. For each selected volumetric flow rate point (V1 and V2), multiple calibrations are performed during production using a specialized median device, and the corresponding upstream and downstream pressure differences (P1 and P2) are recorded. Solve for the normalized coefficients corresponding to flow points V1 and V2 by... Solve for the normalization coefficients corresponding to the upstream and downstream pressure differences P1 and P2. Here, "median value" refers to a representative or standard test piece used in the calibration process to obtain more accurate calibration results. Multiple calibrations are performed and the median value is taken to ensure the stability and accuracy of the calibration results.

[0115] For example, such as Figure 3 The diagram shows the normal and fault characteristics of a particulate filter (GPF). The origin is (0,0), the x-axis is the horizontal axis, and the y-axis is the vertical axis. The horizontal axis represents the volumetric flow rate, and the vertical axis represents the GPF pressure difference in a Cartesian coordinate system. The angle between the line connecting the data point to the origin and the x-axis is θ. Any blue dot represents the data when the GPF is normal, and any red dot represents the data when the GPF is faulty. The volumetric flow rate through the particulate filter is within a first preset range of [500m³ / s]. 3 / h, 1500m 3 Taking [h] as an example, record the values ​​at the two endpoints of the median component within the first preset range, i.e., the volumetric flow rate is 500 m³ / h. 3 / h, 1500m 3 The pressure differences P1 = 10 hPa and P2 = 60 hPa at the given time are calculated based on the volumetric flow rate and corresponding pressure differences at these two endpoints. The corresponding volumetric flow rate normalization coefficient C is then calculated. v and pressure difference normalization coefficient C p ,

[0116] S230. Normalize the exhaust volume flow rate and the upstream and downstream pressure difference, and calculate the angle between the line connecting the normalized data point and the origin in the Cartesian coordinate system and the X-axis.

[0117] S240. When the included angle is less than the first threshold, it is determined that the particle collector has a removal fault.

[0118] Based on the above embodiments, this invention further clarifies how to calibrate the pressure difference normalization coefficient using a particulate filter. By recording the pressure differences (P1 and P2) corresponding to specific flow points (V1 and V2), the corresponding volumetric flow rate normalization coefficient C is calculated.v and pressure difference normalization coefficient C p This provides a volumetric flow rate normalization coefficient C applicable to the pressure difference corresponding to the current flow conditions (V1 and V2) for subsequent normalization processing. v and pressure difference normalization coefficient C p This eliminates the discrepancies between volumetric flow rate data and differential pressure data, providing a standardized normalization standard for subsequent data analysis.

[0119] Figure 4 This is a flowchart illustrating another fault diagnosis method for a particulate filter provided by an embodiment of the present invention. Based on the above embodiments explaining the calibration normalization coefficients of the particulate filter's intermediate components, this embodiment further explains the process of normalizing the exhaust volume flow rate and upstream / downstream pressure difference, and how to calculate the angle between the line connecting the data points and the origin in the Cartesian coordinate system after normalization and the X-axis.

[0120] like Figure 4 As shown, the method includes:

[0121] S310, Obtain the engine's operating conditions.

[0122] S320. When the operating conditions meet the fault diagnosis conditions of the particulate filter, obtain the exhaust volume flow rate of the engine and the pressure difference between the upstream and downstream of the particulate filter.

[0123] S321. Calibrate the normalization coefficient using the median element of the particulate filter. The calibration process of the normalization coefficient includes: recording the upstream and downstream pressure differences P1 and P2 corresponding to exhaust volume flow rates V1 and V2, respectively. Then, the normalization coefficient...

[0124] Given S330, exhaust volumetric flow rate V3, and upstream and downstream pressure difference Dp1, then the normalized exhaust volumetric flow rate... Normalized upstream and downstream pressure difference According to the formula for calculating the included angle Calculate the included angle.

[0125] Specifically, based on the calibrated normalization coefficient C v and C p The current exhaust volume flow rate V3 and its corresponding upstream and downstream pressure difference Dp1 are converted into normalized values, and then the included angle θ is calculated using the included angle calculation formula.

[0126] S330. Normalize the exhaust volume flow rate and the upstream and downstream pressure difference, and calculate the angle between the line connecting the normalized data point and the origin in the Cartesian coordinate system and the X-axis.

[0127] S340. When the included angle is less than the first threshold, it is determined that the particle collector has a removal fault.

[0128] This invention, based on the above embodiments, further clarifies how to calibrate the pressure differential normalization coefficient using a particulate filter. It also explains the process of normalizing the exhaust volume flow rate and upstream / downstream pressure differential, and how to calculate the angle between the line connecting the data points and the origin in the Cartesian coordinate system after normalization and the X-axis. Normalization helps eliminate differences between different operating conditions or equipment (e.g., gasoline vehicles and hybrid vehicles), making the data more comparable. Angle calculation provides an intuitive way to evaluate the relationship between exhaust volume flow rate and upstream / downstream pressure differential.

[0129] Figure 5 A flowchart illustrating another fault diagnosis method for a particle trap provided in an embodiment of the present invention. (See reference...) Figure 5 Based on the above embodiments, this invention describes that when the fault diagnosis conditions of the particle trap are met multiple times, the method calculates the angle between the line connecting the data points obtained from multiple judgments and the origin and the X-axis in the Cartesian coordinate system, and uses this to comprehensively confirm whether the particle trap has a fault.

[0130] like Figure 5 As shown, the method includes:

[0131] S410, Obtain the engine's operating conditions.

[0132] S420. When the operating conditions meet the fault diagnosis conditions of the particulate filter, the exhaust volume flow rate of the engine and the pressure difference between the upstream and downstream of the particulate filter are obtained multiple times.

[0133] Specifically, when the fault diagnosis conditions of the particulate filter are met for the first time, the engine's exhaust volumetric flow rate and the pressure difference between the upstream and downstream sides of the particulate filter are acquired. If the subsequent operating conditions continue to meet the fault diagnosis conditions, the engine's exhaust volumetric flow rate and the corresponding pressure difference between the upstream and downstream sides of the particulate filter are acquired at regular intervals. If the subsequent operating conditions do not meet the fault diagnosis conditions of the particulate filter, the current diagnosis process is considered to have ended, and the process continues to wait for the next time the fault diagnosis conditions of the particulate filter are met.

[0134] S430. Based on multiple sets of exhaust volume flow rate and upstream and downstream pressure difference data, calculate the moving average filter value θ of the included angle. filter .

[0135] Wherein, the moving average filter value θ filter It can be understood as the weighted value of the angle between the line connecting the data points and the origin in the Cartesian coordinate system and the X-axis after normalization of multiple sets of exhaust volume flow rate and upstream and downstream pressure difference data.

[0136] Specifically, if there are multiple sets of exhaust volumetric flow rate and upstream / downstream pressure difference data during a single engine operation, the angle between the line connecting the normalized data points and the origin in the Cartesian coordinate system and the X-axis is calculated for each set of exhaust volumetric flow rate and upstream / downstream pressure difference data. The average filtered value θ is then obtained through weighted calculation. filter .

[0137] For example, suppose that during one engine operation, exhaust volume flow rate and upstream / downstream pressure difference data that meet the particulate filter fault diagnosis conditions are recorded n times. When the particulate filter fault diagnosis conditions are met for the first time (n=1), the normalized exhaust volume flow rate and upstream / downstream pressure difference data are recorded. The angle θ between the line connecting the data point and the origin in the Cartesian coordinate system and the X-axis is calculated. filter (n) = θ; when this condition is satisfied for the nth time (n>1), θ filter (n) represents the weighted value of θ.

[0138] S440, according to θ filter Determine if the particle trap has a removal fault.

[0139] Specifically, by judging θ filter The relationship with the second threshold, when θ filter When the angle is greater than the second threshold, the GPF can be considered to be in normal working condition. When the angle is less than the second threshold, the GPF can be considered to have a removal fault, i.e., the GBF has been removed or damaged and is not working properly. The second threshold has the same physical meaning as the first threshold and applies to θ. filter The value of the judgment can be the same as or different from the first threshold.

[0140] Based on the above embodiments, this invention describes a method that, when multiple operating conditions meet the fault diagnosis conditions of the particulate filter, calculates the angle between the line connecting the obtained data points and the origin in the Cartesian coordinate system and the X-axis, and uses this angle to comprehensively confirm whether the particulate filter is faulty. This is achieved by repeatedly acquiring the engine's exhaust volume flow rate and the upstream and downstream pressure difference of the particulate filter, and calculating the moving average filter value θ of the angle. filter The fault diagnosis method for particle traps provided in this embodiment of the invention can reduce the error caused by a single measurement and improve the accuracy of fault diagnosis.

[0141] In an optional embodiment, the moving average filter value θ of the included angle is calculated based on multiple sets of exhaust volume flow rate and upstream and downstream pressure difference data. filter ,include:

[0142] When the fault diagnosis conditions of the particle trap are met for the first time, n = 1, θ filter (n) = θ; when the fault diagnosis condition of the particle trap is met for the nth time, n>1, θfilter ψ(n) = (1 - C2)×θ filter ψ(n - 1)+C2×θ, where C2 is a calibratable constant parameter;

[0143] Based on θ filter Determine whether there is a removal failure of the particulate filter, including:

[0144] When n > C3, perform a fault determination;

[0145] If at this time θ filter ψ(n)≥C4, it is determined that the diagnosis is completed and there is no particulate filter removal failure;

[0146] If at this time θ filter ψ(n)<C4, it is determined that the diagnosis is completed and there is a particulate filter removal failure;

[0147] Where both C3 and C4 are calibratable constant parameters.

[0148] Specifically, when the fault diagnosis condition of the particulate filter is first met (i.e., when n = 1), the fault diagnosis device of the particulate filter records the measured included angle θ of this time and uses it as the initial value of the moving average filter value, that is, ψ filter (1)=θ.

[0149] When the nth time (n > 1) meets the fault diagnosis condition of the particulate filter, based on the previously calculated moving average filter value ψ filter (n - 1) and the currently measured included angle θ, a new moving average filter value ψ filter (n) is obtained through weighted calculation. The weighted calculation formula is: ψ filter (n) = (1 - C2)×ψ filter (n - 1)+C2×θ. Among them, C2 is a calibratable constant parameter, which determines the weight of the current measured value θ in the moving average filter value ψ filter (n).

[0150] When the number of measurements n is greater than a certain calibratable constant parameter C3, it can be considered that there are enough data points to support an accurate fault determination. When n > C3, based on the currently calculated moving average filter value ψ filter (n) is compared with another calibratable constant parameter C4 to determine the state of the particulate filter. C4 represents the second threshold, and the physical meaning of the second threshold C4 is the same as that of the first threshold, which is applicable to the determination of θ filter judgment, and numerically it can be the same as the first threshold or different from the first threshold.

[0151] If θ filterIf θ(n) ≥ C4, it is determined that the diagnosis is complete and there is no fault in the removal of the particulate filter. This means that the particulate filter shows a normal working state in most cases, and even if there are faults in a few cases, it does not affect normal use.

[0152] If θ filter (n) < C4, it is determined that the diagnosis is complete and there is a fault in the removal of the particulate filter. This indicates that the particulate filter may have been removed or damaged, resulting in its inability to work properly in most cases. Based on comprehensive judgment, the particulate filter cannot operate normally.

[0153] Exemplarily, the default value of C2 can be 0.6, the default value of C3 can be 100, and the default value of C4 can be 10°.

[0154] Based on the above embodiments, the embodiments of the present invention further illustrate the weighted algorithm of θ filter . By introducing the moving average filtering algorithm and the calibratable constant parameters, the accuracy and robustness of the particulate filter fault diagnosis are further improved, the fault diagnosis process is simplified, and the practicability and generality of the system are improved.

[0155] According to the same inventive concept, the embodiments of the present invention provide a fault diagnosis device for a particulate filter, Figure 6 which is a structural block diagram of a fault diagnosis device for a particulate filter provided by the embodiments of the present invention. Refer to Figure 6 , and it includes: an operating condition acquisition module 51, a data acquisition module 52, a calculation module 53, and a fault judgment module 54, where:

[0156] The operating condition acquisition module 51 is used to acquire the operating conditions of the engine;

[0157] The data acquisition module 52 is used to acquire the exhaust gas volume flow rate of the engine and the upstream and downstream pressure differences of the particulate filter when the operating conditions meet the fault diagnosis conditions of the particulate filter;

[0158] The calculation module 53 normalizes the exhaust gas volume flow rate and the upstream and downstream pressure differences, and calculates the angle between the line connecting the data point in the Cartesian coordinate system after normalization and the origin and the X-axis;

[0159] The fault judgment module 54 is used to judge whether there is a removal fault in the particulate filter.

[0160] Optionally, the operating condition acquisition module 51 acquires the operating conditions of the engine, including:

[0161] acquiring the ambient temperature and ambient pressure when the engine is running;

[0162] acquiring whether the charging model of the engine is valid;

[0163] Acquire the duration of engine start-up and the oxygen sensor over-dew point duration;

[0164] To determine whether the engine is in catalytic converter heating mode;

[0165] Determine if the engine is in a fuel cut-off condition;

[0166] Check if the particle collector is in regeneration mode;

[0167] Check if the differential pressure sensor of the particle trap is faulty;

[0168] Obtain the volumetric flow rate range through the particulate filter.

[0169] Optionally, the fault diagnosis conditions of the particle trap shall be met, including:

[0170] The ambient temperature is greater than -7℃;

[0171] Environmental pressure greater than 740 hPa;

[0172] The inflatable model is effective;

[0173] The engine starts and continues for the first preset time;

[0174] The oxygen sensor exceeds the dew point and remains in operation for a second preset time.

[0175] The engine is not in catalytic converter heating mode;

[0176] The engine is not in a fuel cut-off condition;

[0177] The particulate filter is not in regeneration mode;

[0178] The differential pressure sensor of the particulate filter is functioning correctly.

[0179] The volumetric flow rate passing through the particle collector is within a first preset range.

[0180] Optionally, before normalizing the exhaust volume flow rate and the upstream and downstream pressure difference, the calculation module 53 also includes:

[0181] The normalization coefficients are calibrated using the median of the particle trap. The calibration process for the normalization coefficients includes:

[0182] Record the upstream and downstream pressure differences P1 and P2 when the exhaust volume flow rates are V1 and V2, respectively, and then the normalization coefficient is...

[0183] Optionally, the exhaust volume flow rate and upstream / downstream pressure difference can be normalized, including:

[0184] Given exhaust volumetric flow rate V1 and upstream and downstream pressure difference Dp1, the normalized exhaust volumetric flow rate is... Normalized upstream and downstream pressure difference

[0185] Calculation module 53 calculates the angle between the line connecting the data point in the Cartesian coordinate system after normalization and the origin and the X-axis, including:

[0186] According to the angle calculation formula Calculate the angle.

[0187] Optionally, when the operating conditions meet the fault diagnosis conditions of the particulate trap, it further includes:

[0188] Data acquisition module 52 acquires the exhaust gas volume flow rate of the engine and the upstream and downstream pressure differences of the particulate trap multiple times;

[0189] Calculation module 53 calculates the moving average filtered value θ of the angle according to multiple groups of exhaust gas volume flow rate and upstream and downstream pressure difference data filter ;

[0190] Fault judgment module 54 judges whether there is a removal fault in the particulate trap according to θ filter Judge whether there is a removal fault in the particulate trap.

[0191] Optionally, according to multiple groups of exhaust gas volume flow rate and upstream and downstream pressure difference data, calculation module 53 calculates the moving average filtered value θ of the angle filter , including:

[0192] When the fault diagnosis conditions of the particulate trap are met for the first time, n = 1, θ filter (n)=θ; when the fault diagnosis conditions of the particulate trap are met for the nth time, n>1, θ filter (n)=(1 - C2)×θ filter (n - 1)+C2×θ, where C2 is a calibratable constant parameter;

[0193] Fault judgment module 54 judges whether there is a removal fault in the particulate trap according to θ filter Judge whether there is a removal fault in the particulate trap, including:

[0194] When n>C3, perform fault judgment;

[0195] If θ filter (n)≥C4 at this time, judge that the diagnosis is completed and there is no removal fault in the particulate trap;

[0196] If θ filter (n)<C4 at this time, judge that the diagnosis is completed and there is a removal fault in the particulate trap;

[0197] Where C3 and C4 are both calibratable constant parameters.

[0198] The fault diagnosis device for the particulate trap provided in this embodiment of the invention can execute the fault diagnosis method for the particulate trap provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0199] Based on the same inventive concept, embodiments of the present invention provide a fault diagnosis device for a particle trap, comprising:

[0200] One or more processors;

[0201] Memory, used to store one or more programs;

[0202] When one or more programs are executed by one or more processors, the one or more processors implement the fault diagnosis method for the particle trap provided in any embodiment of the present invention.

[0203] Based on the same inventive concept, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a fault diagnosis method for a particle trap provided in any embodiment of the present invention.

[0204] Figure 7 This is a structural schematic diagram of the fault diagnosis device for the particle trap provided in an embodiment of the present invention.

[0205] The particulate filter fault diagnosis device is a design specifically suited for various types of vehicles, including but not limited to passenger cars, commercial vehicles, industrial vehicles, and agricultural machinery. This device may exist in any of the following forms, including but not limited to, integrated into the vehicle's central control system, a standalone electronic control unit, or a standalone diagnostic device. The device utilizes the vehicle's own sensor network, such as exhaust flow sensors and differential pressure sensors, to collect key parameters in real time and process the data through a built-in processor. Furthermore, the system has the ability to communicate with other vehicle electronic modules (such as the engine control unit ECU) and share data through standardized vehicle communication protocols (such as CAN bus, FlexRay, or Ethernet) to achieve more comprehensive fault diagnosis and vehicle performance optimization. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0206] like Figure 7As shown, the fault diagnosis device 10 for the particle trap includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the fault diagnosis device 10 for the particle trap. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0207] Multiple components in the particle trap fault diagnosis device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the particle trap fault diagnosis device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0208] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as fault diagnosis methods for particle traps.

[0209] In some embodiments, the fault diagnosis method for the particle trap can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the fault diagnosis device 10 of the particle trap via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the fault diagnosis method for the particle trap described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the fault diagnosis method for the particle trap by any other suitable means (e.g., by means of firmware).

[0210] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0211] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0212] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0213] To provide user interaction, the systems and techniques described herein can be implemented on a particulate trap diagnostic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the particulate trap diagnostic device. Other types of devices can also be used to provide user interaction; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0214] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0215] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0216] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0217] The specific embodiments described above do not constitute a limitation on the scope of this protection. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of this protection.

Claims

1. A fault diagnosis method for a particle trap, characterized in that, include: Obtain the engine's operating conditions; When the operating conditions meet the fault diagnosis conditions of the particulate filter, the exhaust volume flow rate of the engine and the pressure difference between the upstream and downstream of the particulate filter are obtained. The exhaust volume flow rate and the upstream and downstream pressure difference are normalized, and the angle between the line connecting the normalized data point and the origin in the Cartesian coordinate system and the X-axis is calculated. When the included angle is less than the first threshold, it is determined that the particle collector has a removal fault; Wherein, the X-axis of the Cartesian coordinate system corresponds to the exhaust volume flow rate, and the Y-axis corresponds to the upstream and downstream pressure difference; Before normalizing the exhaust volume flow rate and the upstream and downstream pressure difference, the method further includes: The normalization coefficients are calibrated using the median of the particle trap, wherein the calibration process of the normalization coefficients includes: Record the upstream and downstream pressure differences P1 and P2 when the exhaust volume flow rates are V1 and V2, respectively, and then the normalization coefficient is... , ; Normalizing the exhaust volume flow rate and the upstream and downstream pressure difference includes: The exhaust volume flow rate is V3, and the upstream and downstream pressure difference is Dp1. Therefore, the normalized exhaust volume flow rate is... Normalized upstream and downstream pressure difference ; Calculate the angle between the line connecting the normalized data points and the origin in the Cartesian coordinate system and the X-axis, including: According to the formula for calculating the included angle Calculate the included angle.

2. The fault diagnosis method for a particle trap according to claim 1, characterized in that, Obtain the engine's operating conditions, including: Obtain the ambient temperature and ambient pressure during engine operation; Determine whether the inflation model of the engine is valid; The duration of engine start-up and the duration of oxygen sensor over-dew point are obtained; Determine whether the engine is in catalytic converter heating mode; Determine whether the engine is in a fuel cut-off condition; Determine whether the particle collector is in regeneration mode; To determine if the differential pressure sensor of the particle trap is faulty; Obtain the volumetric flow rate range through the particle trap.

3. The fault diagnosis method for the particle trap according to claim 2, characterized in that, The fault diagnosis conditions for the particle trap include: The ambient temperature is greater than -7℃; Environmental pressure greater than 740 hPa; The inflatable model is effective; The engine starts and continues for the first preset time; The oxygen sensor exceeds the dew point and remains above the second preset time; The engine was not in catalytic converter heating mode; The engine was not in a fuel cut-off condition; The particle collector was not in regeneration mode; The differential pressure sensor of the particle trap is fault-free; The volumetric flow rate through the particle collector is within a first preset range.

4. The fault diagnosis method for the particulate filter according to claim 1, characterized in that, When the operating conditions meet the fault diagnosis conditions of the particle trap, the following is also included: The exhaust volume flow rate of the engine and the pressure difference between the upstream and downstream sides of the particulate filter are obtained multiple times. Based on multiple sets of exhaust volume flow rate and upstream and downstream pressure difference data, the moving average filter value θ of the included angle is calculated. filter ; According to θ filter Determine if the particle trap has a removal fault.

5. The fault diagnosis method for the particle trap according to claim 4, characterized in that, Based on multiple sets of exhaust volume flow rate and upstream and downstream pressure difference data, the moving average filter value θ of the included angle is calculated. filter ,include: When the fault diagnosis condition of the particle trap is met for the first time, n=1, θ filter (n) = θ; when the fault diagnosis condition of the particle trap is met for the nth time, n > 1, θ filter (n)=(1-C2)×θ filter (n-1)+C2×θ, where C2 is a calibrable constant parameter; According to θ filter Determining whether the particle trap has a removal fault includes: When n > C3, fault diagnosis is performed; If θ at this time filter (n)≥C4, indicating that the diagnosis is complete and there is no fault in the removal of the particle trap; If θ at this time filter (n) < C4, it is determined that the diagnosis is completed and there is a fault in the removal of the particulate filter; C3 and C4 are both calibrable constant parameters.

6. A fault diagnosis device for a particle trap, characterized in that, For implementing the fault diagnosis method for a particulate trap as described in any one of claims 1 to 5, the fault diagnosis device for the particulate trap includes: The operating condition acquisition module is used to acquire the engine's operating conditions; The data acquisition module is used to acquire the exhaust volume flow rate of the engine and the upstream and downstream pressure difference of the particulate filter when the operating conditions meet the fault diagnosis conditions of the particulate filter. The calculation module normalizes the exhaust volume flow rate and the upstream and downstream pressure difference, and calculates the angle between the line connecting the normalized data point and the origin in the Cartesian coordinate system and the X-axis. The fault diagnosis module is used to determine whether the particle collector has a removal fault.

7. A fault diagnosis device for a particle trap, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the fault diagnosis method for the particle trap as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the fault diagnosis method for the particle trap as described in any one of claims 1 to 5.

Images