Fault diagnosis method, device and equipment for particle trap and storage medium

Abstract

The embodiment of the invention provides a fault diagnosis method, device and equipment for a particulate filter and a storage medium. The fault diagnosis method for the particulate filter comprises the steps that the operation condition of an engine is obtained; when the operation condition meets the fault diagnosis condition of the particulate filter, the exhaust volume flow of the engine and the upstream and downstream pressure difference of the particulate filter are obtained; normalizing the exhaust volume flow and the upstream and downstream pressure difference, and calculating an included angle between a connecting line of a data point and an original point in a Cartesian coordinate system after normalization and an X axis; when the included angle is smaller than a first threshold value, it is judged that the particle trap has a removal fault; wherein the X axis of the Cartesian coordinate system corresponds to the exhaust volume flow, and the Y axis corresponds to the upstream and downstream pressure difference. By adopting the technical scheme, the universality and the accuracy of the fault diagnosis strategy of the particulate filter are improved, and the problem that the diagnosis strategies of a traditional fuel vehicle and a hybrid vehicle are difficult to unify is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor vehicle particulate traps, and in particular to a fault diagnosis method, device, equipment and storage medium for a particulate trap. Background Art

[0002] The particulate filter is a device installed in the vehicle engine emission system. It is a ceramic filter that mainly filters the fine particulate emissions in the engine exhaust. For gasoline engines, when the particulate matter in the gasoline particulate filter (GPF) accumulates to a certain level, the burner will automatically ignite and burn the carbon soot particles adsorbed on it, turning them into carbon dioxide for discharge, thus optimizing its own performance.

[0003] The current GPF diagnostic system works by installing a differential pressure sensor. It builds a model based on the differential pressure flow characteristics of the GPF. In steady state, if the actual differential pressure is much lower than the model prediction or the set threshold, the GPF is judged to be removed. In dynamic conditions, if the measured differential pressure gradient is much lower than the model gradient, the GPF is also judged to be missing. However, this process simplifies the estimation of the temperature field and pressure field of the GPF, resulting in large calculation errors, and the actual differential pressure and volume flow are only correlated rather than precise.

[0004] In addition, the engine of a traditional fuel vehicle is in a steady-state condition less often and in a dynamic condition more often when driving, while the hybrid vehicle is just the opposite. If the fuel version and the hybrid version of the same vehicle model use the same diagnostic strategy, one of the models will find it difficult to meet the regulatory requirements for in-use monitoring frequency. If different diagnostic strategies are used, the calibration data of the GPF diagnosis in the engine control unit will have to be managed separately, causing inconvenience. Summary of the invention

[0005] The embodiments of the present invention provide a fault diagnosis method, device, equipment and storage medium for a particulate filter, which improve the versatility and accuracy of the fault diagnosis strategy for the particulate filter and solve the problem of difficulty in unifying the diagnosis 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] Obtaining the operating conditions of the engine;

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

[0009] Normalize the exhaust volume flow rate and the upstream and downstream pressure difference, and calculate the angle between the normalized data point and the origin in the Cartesian coordinate system and the X-axis;

[0010] When the included angle is less than a first threshold, it is determined that a removal failure exists in the particle trap;

[0011] 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.

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

[0013] Obtain the ambient temperature and ambient pressure when the engine is running;

[0014] Get whether the engine's inflation model is valid;

[0015] Obtain the duration after engine start and the duration of oxygen sensor over dew point;

[0016] Obtain whether the engine is in a catalyst heating condition;

[0017] Get whether the engine is in a fuel cut-off condition;

[0018] Get whether the particulate filter is in the regeneration condition;

[0019] Check whether the differential pressure sensor of the particulate filter is faulty;

[0020] Obtain the volume flow range through the particle trap.

[0021] Optionally, meet the fault diagnosis conditions of the particulate filter, including:

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

[0023] The ambient pressure is greater than 740hPa;

[0024] Inflatable models work;

[0025] The engine is started and lasts for a first preset time;

[0026] The oxygen sensor exceeds the dew point and lasts for a second preset time;

[0027] The engine is not in the catalyst heating state;

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

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

[0030] The differential pressure sensor of the particulate trap is not faulty;

[0031] The volume flow rate flowing through the particle trap is within a first preset range.

[0032] Optionally, before normalizing the exhaust volume flow rate and the upstream and downstream pressure difference, it also includes:

[0033] The normalization coefficient is calibrated using the median part of the particle collector, wherein the calibration process of the normalization coefficient includes:

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

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

[0036] The exhaust volume flow is V3 and the upstream and downstream pressure difference is Dp1, then the normalized exhaust volume flow Normalized upstream and downstream pressure difference

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

[0038] According to the angle calculation formula Calculate the angle.

[0039] Optionally, when the operating conditions meet the fault diagnosis conditions of the particulate filter, it also includes:

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

[0041] According to multiple sets of exhaust volume flow and upstream and downstream pressure difference data, the moving average filter value θ of the angle is calculated filter ;

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

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

[0044] When the fault diagnosis condition of the particulate filter is met for the first time, n = 1, θ filter (n) = θ; when the fault diagnosis condition of the particulate filter 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 Determine whether the particulate collector has a removal failure, including:

[0046] When n>C3, perform fault judgment;

[0047] If at this time θfilter (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] Where 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 differences 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 differences, and calculates the angle between the line connecting the data points in the Cartesian coordinate system after normalization and the origin and the X-axis;

[0054] A fault judgment 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 in 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, the fault diagnosis method for a particulate filter provided in any embodiment of the present invention is implemented.

[0060] The fault diagnosis method for the particulate filter provided in the embodiment of the present invention obtains the operating conditions of the engine; when the operating conditions meet the fault diagnosis conditions of the particulate filter, obtains the exhaust volume flow rate of the engine and the upstream and downstream pressure difference of the particulate filter; normalizes the exhaust volume flow rate and the upstream and downstream pressure difference, and calculates the angle between the normalized data point and the origin line in the Cartesian coordinate system and the X-axis; when the angle is less than the 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 volume flow rate, and the Y-axis corresponds to the upstream and downstream pressure difference. The embodiment of the present invention proposes an innovative fault diagnosis method for the particulate filter, which does not need to rely on complex pressure difference model calibration, nor does it need to set different monitoring frequencies for fuel and hybrid vehicles, thereby significantly improving the versatility and applicability of the fault diagnosis strategy. This improvement effectively solves the problem that it is difficult to unify the diagnostic strategies of traditional fuel vehicles and hybrid vehicles, ensuring that both can meet the in-use monitoring frequency requirements stipulated by regulations.

[0061] It should be understood that the content described in this section is not intended to identify the key or important 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 easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solution in this embodiment, the drawings required for use in the description of the embodiment will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

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

[0064] Figure 2 A flow chart of another method for diagnosing a fault of a particle trap provided by an embodiment of the present invention;

[0065] Figure 3 A schematic diagram of normal GPF and GPF fault characteristics of a particulate filter provided by an embodiment of the present invention;

[0066] Figure 4 A flow chart of another method for diagnosing a fault of a particle trap provided by an embodiment of the present invention;

[0067] Figure 5 A flow chart of another method for diagnosing a fault of a particle trap provided by an embodiment of the present invention;

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

[0069] Figure 7 It is a schematic diagram of the structure of a fault diagnosis device for a particle collector provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0071] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0072] Figure 1 This is a flow chart of a particulate trap fault diagnosis method provided by an embodiment of the present invention. This embodiment can be applied to determine whether there is a need to remove a particulate trap fault when the operating conditions meet the particulate trap fault diagnosis conditions. This method can be performed by a particulate trap fault diagnosis device provided by an embodiment of the present invention. The particulate trap fault diagnosis device can be implemented in the form of hardware and / or software. The particulate trap fault diagnosis device can be configured in the particulate trap fault diagnosis device provided by an embodiment of the present invention. Figure 1 As shown, the method includes:

[0073] S110: Obtain engine operating conditions.

[0074] Among them, the operating conditions of the engine can be understood as: the operating conditions of the engine that comprehensively manage the engine, including the engine's 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 include the engine and its operating status, and the particulate filter and its operating status.

[0076] In an optional embodiment, obtaining the operating condition of the engine includes:

[0077] Obtain the ambient temperature and ambient pressure when the engine is running;

[0078] Get whether the engine's inflation model is valid;

[0079] Obtain the duration after engine start and the duration of oxygen sensor over dew point;

[0080] Obtain whether the engine is in a catalyst heating condition;

[0081] Get whether the engine is in a fuel cut-off condition;

[0082] Get whether the particulate filter is in the regeneration condition;

[0083] Check whether the differential pressure sensor of the particulate filter is faulty;

[0084] Obtain the volume flow range through the particle trap.

[0085] Among them, the ambient temperature when the engine is running can be understood as the atmospheric temperature of the engine's external environment; the ambient pressure when the engine is running can be understood as the atmospheric pressure of the engine's external environment; the engine's charging model can be understood as a model that describes the engine's charging process, and the main function of the charging model is to accurately calculate the charging mass in the combustion chamber, and then determine the required amount of fuel injection; obtaining the duration after the engine is started can be understood as obtaining the time from the engine start to the current moment through the timer; the oxygen sensor dew point duration can be understood as the time from the oxygen sensor reaching the dew point temperature to the current moment obtained by the timer, and the dew point temperature is the time required for the oxygen sensor to pass the dew point temperature. Point temperature refers to the critical temperature at which water vapor in the exhaust system will no longer condense; the catalyst heating condition can be understood as reading the data of the engine water temperature sensor to determine whether the current water temperature is within the range required for the catalyst heating condition; the fuel cut-off condition can be understood as a working mode in which the engine temporarily cuts off the fuel supply under specific conditions; the regeneration condition can be understood as an operating mode in which the GPF is restored to its initial state by burning the particles in the GPF; the pressure difference of the particulate filter can be understood as the pressure difference between the inlet and outlet of the GPF; the volume 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 whether the particulate filter is faulty, it is necessary to first determine whether the particulate filter-related components in the vehicle are operating normally. The following conditions are used to help determine whether the engine is in normal working condition, whether the exhaust emissions are normal, and whether the particulate filter has normal working conditions. The ambient temperature and ambient pressure when the engine is running will affect the engine's operating condition and exhaust emission characteristics. For example, a low temperature environment may make the GPF regeneration process difficult because regeneration requires a higher temperature to incinerate the accumulated particulate matter. Similarly, changes in ambient pressure may also affect the flow rate of exhaust gas and the capture efficiency of GPF. The effectiveness of the engine's charging model is directly related to the engine's intake efficiency and power performance. If the charging model fails, it may cause insufficient engine intake, which in turn affects combustion efficiency and exhaust emissions. The duration after the engine is started and the duration of the oxygen sensor over the dew point can reflect the engine's preheating and stable state. The duration of the oxygen sensor over the dew point is related to the oxygen content in the exhaust gas and the emission quality. The engine's catalyst heating condition affects the exhaust preheating process. If the catalyst fails to heat normally, it may affect its conversion efficiency, resulting in excessive exhaust emissions. At the same time, the heating process of the catalyst may also affect the regeneration temperature and efficiency of the GPF. Under the fuel cut-off condition of the engine, the engine will temporarily cut off the fuel supply. This will lead to a reduction in exhaust emissions, and because regeneration requires a certain amount of fuel combustion to produce high-temperature incineration of particulate matter, the GPF will not be able to regenerate under the fuel cut-off condition. The regeneration condition is an important process for the GPF to remove accumulated particulate matter. During the regeneration condition, it will be impossible to determine whether the particulate filter is faulty. The differential pressure sensor is a key component for monitoring the degree of GPF blockage. If the sensor fails, it will not be able to accurately reflect the blockage condition of the GPF, thereby affecting the fault diagnosis of the particulate filter. The volume flow range reflects the size of the exhaust flow through the GPF, which directly affects the capture efficiency and regeneration requirements of the GPF. If the volume flow is too large, it may cause the GPF to be quickly blocked; conversely, if the volume flow is too small, it may affect the normal capture and regeneration efficiency of the GPF.

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

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

[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 ruled out, and the particulate filter fault diagnosis process will be formally entered. By obtaining the exhaust volume flow rate of the engine and the upstream and downstream pressure difference 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 condition of the particulate trap includes:

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

[0092] The ambient pressure is greater than 740hPa;

[0093] Inflatable models work;

[0094] The engine is started and lasts for a first preset time;

[0095] The oxygen sensor exceeds the dew point and lasts for a second preset time;

[0096] The engine is not in the catalyst heating state;

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

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

[0099] The differential pressure sensor of the particulate trap is not faulty;

[0100] The volume flow rate flowing through the particle trap is within a first preset range.

[0101] Among them, the first preset time can be understood as the minimum time preset in the fault diagnosis device of the particulate filter to ensure that the engine has been running stably and accumulated enough particulate matter; the second preset time can be understood as the time judgment node preset in the fault diagnosis device of the particulate filter to ensure that the water vapor in the engine exhaust gas has condensed and is running stably; the first preset range can be understood as the time judgment node preset in the fault diagnosis device of the particulate filter to judge that the volume flow rate flowing through the particulate filter is within a flow range that can ensure the accuracy of the diagnosis result.

[0102] For example, the engine starts and lasts for a first preset time T1, T1 is a calibrable parameter, and the default value is 10s; the oxygen sensor exceeds the dew point and lasts for a second preset time T2, T2 is a calibrable parameter, and the default value is 3s; the volume flow rate flowing through the particulate filter is in a first preset range of [T3, T4], T3 and T4 are calibrable parameters, and the default values ​​are 500m 3 / h、1500m 3 / h.

[0103] Specifically, if the ambient temperature is greater than -7°C and the ambient pressure is greater than 740hPa, it means that the particulate filter is in normal working temperature and pressure; if the charging model is effective, it means that the engine intake is normal; if the engine starts and lasts for the first preset time, the oxygen sensor exceeds the dew point and lasts for the second preset time, the engine is not in the catalyst heating condition, and the engine is not in the fuel cut-off condition, it means that the engine is exhausting normally and the state is stable; if the particulate filter is not in the regeneration condition, the differential pressure sensor of the particulate filter has no fault, and the volume flow rate flowing through the particulate filter is within the first preset range, it means that the particulate filter is operating normally. The above-mentioned fault diagnosis conditions of the particulate filter are comprehensively determined from multiple angles such as the working principle of the particulate filter, the needs of fault diagnosis, and the influence of environmental factors. If the above-mentioned fault diagnosis conditions are not met, it does not mean that the particulate filter cannot work normally, but the particulate filter may have performance degradation, which will cause the fault diagnosis self-check process of the particulate filter to be unable to determine whether the fault of the particulate filter is caused by its own damage or external factors. Therefore, the fault diagnosis process of the particulate filter will be run only when the above-mentioned conditions are met.

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

[0105] Specifically, the exhaust volume flow rate is recorded as V3, the GPF pressure difference is Dp1, and the exhaust volume flow normalization coefficient is recorded as C v , the pressure difference normalization coefficient is recorded as C p , a Cartesian coordinate system is established with (0,0) as the origin, x as the horizontal axis, and y as the vertical axis. The horizontal axis represents the volume 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 and the origin and the X-axis is θ=arctan(Dp2 / V4). In the formula, arctan() is the inverse tangent function.

[0106] S140: When the included angle is less than a first threshold, it is determined that a removal failure of the particulate trap exists.

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

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

[0109] This embodiment first obtains the operating conditions of the engine to ensure that before checking whether the particulate filter is faulty, it first verifies whether the key components related to the particulate filter (such as the engine) are in normal working condition. Then, when the operating conditions meet the fault diagnosis conditions of the particulate filter, the exhaust volume flow rate of the engine and the upstream and downstream pressure difference of the GPF are obtained to further confirm whether the GPF is in a diagnosable state, and obtain key data for determining whether the GPF has a removal fault. Subsequently, the exhaust volume flow rate and the upstream and downstream pressure difference are normalized, and the angle between the normalized data point and the origin line in the Cartesian coordinate system and the X-axis is calculated. The working condition of the GPF is reflected by calculating the angle. Finally, the angle is determined to determine whether the GPF has a removal fault. If the angle is less than the preset first threshold, it can be considered that the GPF has been removed or damaged, and corresponding maintenance measures need to be taken. The embodiment of the present invention provides a comprehensive, accurate and efficient particulate filter fault diagnosis method. By comprehensively considering the engine operating conditions and the GPF working status, and adopting normalization and angle calculation methods, accurate diagnosis of GPF performance and fault warning are achieved, thereby improving the accuracy and reliability of particulate filter fault diagnosis, thereby reducing the maintenance time and maintenance costs of the particulate filter, and improving the overall performance and reliability of the particulate filter.

[0110] Figure 2 A flowchart of another method for diagnosing a fault of a particle trap provided by an embodiment of the present invention. Figure 3 A schematic diagram of normal GPF and GPF fault characteristics of a particulate filter provided by an embodiment of the present invention. Based on the above embodiment, the embodiment of the present invention describes how to use the particulate filter median to calibrate the normalization coefficient before normalizing the exhaust volume flow and the upstream and downstream pressure difference. Figure 2 As shown, the method includes:

[0111] S210: Obtain the operating conditions of the engine.

[0112] S220: When the operating condition satisfies the fault diagnosis condition of the particulate trap, the exhaust volume flow rate of the engine and the upstream and downstream pressure difference of the particulate trap are obtained.

[0113] S221, using the particulate filter median to calibrate the normalization coefficient, wherein the calibration process of the normalization coefficient includes: recording the corresponding upstream and downstream pressure differences P1 and P2 when the exhaust volume flow rate is V1 and V2, respectively, then the normalization coefficient

[0114] Specifically, by selecting two flow points V1 and V2 of the volume flow rate flowing through the particle collector in the first preset range, the two flow points should be able to represent the typical flow conditions in the preset range. Exemplary V1 and V2 can be two end points of the first preset range. For each selected volume flow point (V1 and V2), a special median component is used to perform multiple calibrations during the production process, and the corresponding upstream and downstream pressure differences (P1 and P2) are recorded. Solve the normalization coefficients corresponding to flow points V1 and V2 by Solve the normalization coefficients corresponding to the upstream and downstream pressure differences P1 and P2. The "median piece" here refers to a representative or standard test piece used in the calibration process to obtain more accurate calibration results. The stability and accuracy of the calibration results are ensured by calibrating multiple times and taking the middle value.

[0115] For example, Figure 3 The normal GPF and GPF fault characteristics of a particulate filter are shown in the figure, with (0,0) as the origin, x as the horizontal axis, y as the vertical axis, the horizontal axis represents the volume flow, the vertical axis represents the Cartesian coordinate system of the GPF pressure difference, the angle between the data point and the origin and the X-axis is θ, any blue point represents the data when GPF occurs, and any red point represents the data when GPF fails. The volume flow rate flowing through the particulate filter is in the first preset range of [500m 3 / h, 1500m 3 / h] as an example, the two endpoint values ​​of the median value in the first preset range are recorded respectively, that is, the volume flow rate is 500m 3 / h、1500m 3 / h corresponding to the pressure difference P1 = 10hpa, P2 = 60hpa, according to the volume flow rate and corresponding pressure difference of the above two endpoint values, calculate the corresponding volume flow normalization coefficient C v and pressure difference normalization coefficient C p ,

[0116] S230, normalizing the exhaust volume flow rate and the upstream and downstream pressure difference, and calculating the angle between 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 a removal failure of the particulate trap exists.

[0118] Based on the above embodiments, the embodiment of the present invention further clarifies how to use the particle collector to calibrate the pressure difference normalization coefficient, and calculates the corresponding volume flow normalization coefficient C by recording the pressure difference (P1 and P2) corresponding to a specific flow point (V1 and V2).v and pressure difference normalization coefficient C p The volume flow normalization coefficient C corresponding to the pressure difference of the current flow condition (V1 and V2) is provided for the subsequent normalization processing. v and pressure difference normalization coefficient C p , eliminating the differences between volume flow data and pressure difference data, and providing a standardized normalization processing standard for subsequent data analysis.

[0119] Figure 4 A flow chart of another method for diagnosing a fault of a particulate filter provided by an embodiment of the present invention. Based on the above-mentioned embodiment of the particulate filter midpoint component calibration normalization coefficient, the embodiment of the present invention further describes the process of normalizing the exhaust volume flow rate and the upstream and downstream pressure difference, and how to calculate the angle between the data point and the origin line in the Cartesian coordinate system and the X-axis after normalization.

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

[0121] S310: Obtain engine operating conditions.

[0122] S320: When the operating condition satisfies the fault diagnosis condition of the particulate trap, the exhaust volume flow rate of the engine and the upstream and downstream pressure difference of the particulate trap are obtained.

[0123] S321, using the particulate filter median to calibrate the normalization coefficient, wherein the calibration process of the normalization coefficient includes: recording the corresponding upstream and downstream pressure differences P1 and P2 when the exhaust volume flow rate is V1 and V2, respectively, then the normalization coefficient

[0124] S330, the exhaust volume flow is V3, the upstream and downstream pressure difference is Dp1, then the normalized exhaust volume flow Normalized upstream and downstream pressure difference According to the angle calculation formula Calculate the angle.

[0125] Specifically, based on the calibrated normalization coefficient C v and C p , convert the current exhaust volume flow rate V3 and its corresponding upstream and downstream pressure difference Dp1 into a normalized value, and then use the angle calculation formula to calculate the angle θ.

[0126] S330, normalizing the exhaust volume flow rate and the upstream and downstream pressure difference, and calculating the angle between 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 a removal failure of the particulate trap exists.

[0128] Based on the above embodiments, the embodiments of the present invention further clarify how to use the particulate filter to calibrate the pressure difference normalization coefficient. Based on the above embodiments, the embodiments of the present invention further explain how to normalize the exhaust volume flow rate and the upstream and downstream pressure difference, and how to calculate the angle between the data point and the origin line in the Cartesian coordinate system and the X-axis after normalization. Normalization helps to eliminate differences between different operating conditions or equipment (such as gasoline vehicles and hybrid vehicles), making the data more comparable. Angle calculation provides an intuitive way to evaluate the relationship between the exhaust volume flow rate and the upstream and downstream pressure difference.

[0129] Figure 5 A flowchart of another method for diagnosing a fault of a particle trap provided by an embodiment of the present invention. Figure 5 Based on the above embodiments, the embodiment of the present invention explains that when multiple operating conditions meet the fault diagnosis conditions of the particulate filter, the method will calculate the angle between the data points obtained by multiple judgments and the line connecting the origin and the X-axis in the Cartesian coordinate system, and comprehensively confirm whether there is a fault in the particulate filter based on this.

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

[0131] S410: Obtain the operating conditions of the engine.

[0132] S420: When the operating condition satisfies the fault diagnosis condition of the particulate trap, the exhaust volume flow rate of the engine and the upstream and downstream pressure difference of the particulate trap are obtained multiple times.

[0133] Specifically, when the fault diagnosis condition of the particulate trap is met for the first time, the exhaust volume flow of the engine and the upstream and downstream pressure difference of the particulate trap are obtained. If the subsequent operating conditions continue to meet the fault diagnosis condition of the particulate trap, the exhaust volume flow of the engine and the corresponding upstream and downstream pressure difference of the particulate trap are obtained once every period of time. If the subsequent operating conditions do not meet the fault diagnosis condition of the particulate trap, the current diagnosis process is deemed to be completed, and the next time the fault diagnosis condition of the particulate trap is met is continued.

[0134] S430, calculating the moving average filter value θ of the angle according to multiple sets of exhaust volume flow and upstream and downstream pressure difference data filter .

[0135] Among them, the moving average filter value θ filter It can be understood as the weighted value of the angle between the data point and the origin line in the Cartesian coordinate system and the X-axis after the multiple sets of exhaust volume flow and upstream and downstream pressure difference data are calculated and normalized.

[0136] Specifically, if there are multiple sets of exhaust volume flow and upstream and downstream pressure difference data during one engine operation, the angle between the data point and the origin in the Cartesian coordinate system and the X-axis after normalization of each exhaust volume flow and upstream and downstream pressure difference data is calculated, and the average filtering value θ is obtained by weighted calculation. filter .

[0137] For example, assuming that a total of n exhaust volume flow and upstream and downstream pressure difference data satisfying the fault diagnosis condition of the particulate trap are recorded during one engine operation, when the fault diagnosis condition of the particulate trap is satisfied for the first time (n=1), the exhaust volume flow and upstream and downstream pressure difference data are recorded to calculate the angle θ between the line connecting the data point and the origin in the Cartesian coordinate system after normalization and the X-axis, θ filter (n) = θ; when the nth time (n>1) is satisfied, θ filter (n) is the weighted value of θ.

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

[0139] Specifically, by judging θ filter The relationship with the second threshold value is filter When the angle is greater than the second threshold, it can be judged that the GPF is in normal working condition. When the angle is less than the second threshold, it can be judged that the GPF has a removal failure, that is, the GBF is removed or damaged and fails to work normally. The second threshold has the same physical meaning as the first threshold and is applicable to θ filter The judgment may be the same as or different from the first threshold in value.

[0140] The embodiment of the present invention, based on the above embodiment, illustrates that when multiple operating conditions all meet the fault diagnosis conditions of the particulate filter, the method will calculate the angle between the data points obtained by multiple judgments and the line connecting the origin and the X-axis in the Cartesian coordinate system, and comprehensively confirm whether the particulate filter has a fault based on this. By obtaining the exhaust volume flow rate of the engine and the upstream and downstream pressure difference of the particulate filter multiple times, and calculating the moving average filter value θ of the angle filter The fault diagnosis method for the particle collector provided by the embodiment of the present 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 filtered value θ of the angle is calculated based on multiple sets of exhaust volume flow and upstream and downstream pressure difference data. filter ,include:

[0142] When the fault diagnosis condition of the particulate filter is met for the first time, n = 1, θ filter (n) = θ; when the fault diagnosis condition of the particulate filter 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 complete 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 complete and there is a particulate filter removal failure;

[0147] where C3 and C4 are both 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 moving average filter value ψ filter (n - 1) calculated previously 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, and it is applicable to the determination of θ filter and can be the same as or different from the first threshold in value.

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

[0152] If θ filter (n) < C4, it is determined that the diagnosis is complete and there is a particulate filter removal fault. This indicates that the particulate filter may have been removed or damaged, resulting in its inability to work properly in most cases. It is comprehensively judged that the particulate filter can no longer 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 a moving average filtering algorithm and 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 configured to acquire the operating conditions of the engine;

[0157] The data acquisition module 52 is configured 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 satisfy 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 configured to judge whether there is a removal fault of 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] Obtain the duration after engine start and the duration of oxygen sensor over dew point;

[0164] Obtain whether the engine is in a catalyst heating condition;

[0165] Get whether the engine is in a fuel cut-off condition;

[0166] Get whether the particulate filter is in the regeneration condition;

[0167] Check whether the differential pressure sensor of the particulate filter is faulty;

[0168] Obtain the volume flow range through the particle trap.

[0169] Optionally, meet the fault diagnosis conditions of the particulate filter, including:

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

[0171] The ambient pressure is greater than 740hPa;

[0172] Inflatable models work;

[0173] The engine is started and lasts for a first preset time;

[0174] The oxygen sensor exceeds the dew point and lasts for a second preset time;

[0175] The engine is not in the catalyst heating state;

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

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

[0178] The differential pressure sensor of the particulate trap is not faulty;

[0179] The volume flow rate flowing through the particle trap 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 further includes:

[0181] The normalization coefficient is calibrated using the median part of the particle collector, wherein the calibration process of the normalization coefficient includes:

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

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

[0184] The exhaust volume flow is V1 and the upstream and downstream pressure difference is Dp1, then the normalized exhaust volume flow Normalized upstream and downstream pressure difference

[0185] The 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] The 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] The 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] The fault judgment module 54 judges whether there is a removal fault of the particulate trap according to θ filter Judge whether there is a removal fault of the particulate trap.

[0191] Optionally, according to multiple groups of exhaust gas volume flow rate and upstream and downstream pressure difference data, the 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 first met, 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] The fault judgment module 54 judges whether there is a removal fault of the particulate trap according to θ filter Judge whether there is a removal fault of 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 of the particulate trap;

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

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

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

[0199] According to the same inventive concept, an embodiment of the present invention provides a fault diagnosis device for a particle trap, comprising:

[0200] one or more processors;

[0201] A memory for storing 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 particulate filter provided by any embodiment of the present invention.

[0203] According to the same inventive concept, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the fault diagnosis method for the particulate filter provided by any embodiment of the present invention is implemented.

[0204] Figure 7 It is a schematic diagram of the structure of a fault diagnosis device for a particle collector provided in an embodiment of the present invention.

[0205] The fault diagnosis device for the particulate filter is a design specifically suitable for various types of vehicles, including but not limited to passenger cars, commercial vehicles, industrial vehicles and agricultural machinery. The device may exist in any of the following forms, including but not limited to, integrated into the vehicle's central control device, an independent electronic control unit, an independent diagnostic device, etc. The device uses the vehicle's own sensor network, such as exhaust flow sensors, differential pressure sensors, etc., to collect key parameters in real time and process the data through a built-in processor. In addition, the system also has the ability to communicate with other electronic modules of the vehicle (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 in this article, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0206] like Figure 7As shown, the fault diagnosis device 10 of the particulate trap includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the fault diagnosis device 10 of the particulate trap can also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

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

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

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

[0210] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0211] Computer programs for implementing 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

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

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

[0214] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0215] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0216] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0217] The above specific implementations do not constitute a limitation on the scope of protection of this invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of this invention shall be included in the scope of protection of this invention.

Claims

1. A fault diagnosis method for a particle collector, characterized in that: include: Obtaining the operating conditions of the engine; When the operating condition satisfies the fault diagnosis condition of the particulate trap, acquiring the exhaust volume flow rate of the engine and the upstream and downstream pressure difference of the particulate trap; Normalizing the exhaust volume flow rate and the upstream and downstream pressure difference, and calculating the angle between 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 particle trap has a removal failure; 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.

2. The fault diagnosis method for a particulate filter according to claim 1, characterized in that: Obtain engine operating conditions, including: Acquire the ambient temperature and ambient pressure when the engine is running; Obtaining whether the charging model of the engine is valid; Obtaining the duration after the engine is started and the duration of the oxygen sensor exceeding the dew point; Obtaining whether the engine is in a catalyst heating condition; Obtaining whether the engine is in a fuel cut-off condition; obtaining whether the particle trap is in a regeneration condition; obtaining whether a differential pressure sensor of the particulate trap is faulty; The volume flow rate range flowing through the particle trap is obtained.

3. The fault diagnosis method for a particulate filter according to claim 2, characterized in that: The fault diagnosis conditions of the particulate filter are met, including: The ambient temperature is greater than -7°C; The ambient pressure is greater than 740hPa; Inflatable models work; The engine is started and lasts for a first preset time; The oxygen sensor exceeds the dew point and lasts for a second preset time; The engine is not in a catalyst heating condition; The engine is not in a fuel cut-off condition; The particulate trap is not in a regeneration condition; The differential pressure sensor of the particle trap is fault-free; The volume flow rate flowing through the particle trap is within a first preset range.

4. The fault diagnosis method for a particulate filter according to claim 1, characterized in that: Before normalizing the exhaust volume flow rate and the upstream and downstream pressure difference, the method further includes: The normalization coefficient is calibrated using the particle collector median component, wherein the calibration process of the normalization coefficient includes: Record the upstream and downstream pressure differences P1 and P2 when the exhaust volume flow rate is V1 and V2 respectively, then the normalization coefficient 5. The fault diagnosis method for a particulate filter according to claim 4, characterized in that: Normalizing the exhaust volume flow rate and the upstream and downstream pressure difference includes: The exhaust volume flow is V3 and the upstream and downstream pressure difference is Dp1, then the normalized exhaust volume flow Normalized upstream and downstream pressure difference Calculate the angle between the line connecting the data point and the origin in the Cartesian coordinate system after normalization and the X-axis, including: According to the angle calculation formula Calculate the angle.

6. The fault diagnosis method for a particulate filter according to claim 1, characterized in that: When the operating condition meets the fault diagnosis condition of the particulate trap, the method further includes: Acquiring the exhaust volume flow rate of the engine and the upstream and downstream pressure difference of the particulate trap multiple times; According to multiple sets of exhaust volume flow and upstream and downstream pressure difference data, the moving average filter value θ of the angle is calculated filter ; According to θ filter It is determined whether the particulate trap has a removal failure.

7. The fault diagnosis method for a particulate trap according to claim 6, characterized in that: According to multiple sets of exhaust volume flow and upstream and downstream pressure difference data, the moving average filter value θ of the angle is calculated filter ,include: When the fault diagnosis condition of the particulate trap is met for the first time, n=1, θ filter (n) = θ; when the fault diagnosis condition of the particulate 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 failure includes: When n>C3, perform fault judgment; If at this time θ filter (n) ≥C4, the diagnosis is judged to be complete and there is no particle trap removal failure; If θ at this time filter (n) < C4, it is determined that the diagnosis is completed and there is a particulate filter removal failure; Among them, C3 and C4 are both calibrable constant parameters.

8. A fault diagnosis device for a particle collector, characterized in that: include: An operating condition acquisition module, used to acquire the operating conditions of the engine; a data acquisition module, configured to acquire the exhaust volume flow of the engine and the upstream and downstream pressure difference of the particulate trap when the operating condition satisfies the fault diagnosis condition of the particulate trap; A calculation module, normalizing the exhaust volume flow and the upstream and downstream pressure difference, and calculating the angle between the normalized data point and the origin in the Cartesian coordinate system and the X-axis; The fault judgment module is used to judge whether the particle collector has a removal fault.

9. A fault diagnosis device for a particle collector, characterized in that: include: one or more processors; A memory for storing 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 particulate trap as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the fault diagnosis method for the particulate trap according to any one of claims 1 to 7 is implemented.

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