Diagnostic method, device, engine and vehicle for identifying DPF failure risk

Abstract

The present application relates to the technical field of engine after-treatment, and in particular to a diagnostic method, device, engine, and vehicle for identifying the risk of DPF failure, wherein the method comprises: S1, obtaining a first particulate matter concentration and a second particulate matter concentration; S2, determining a particulate matter concentration ratio based on the first particulate matter concentration and the second particulate matter concentration; S3, determining the type of failure based on the particulate matter concentration ratio and the second particulate matter concentration. The present application sets a particulate matter concentration sensor at the air inlet and outlet of the DPF, and determines whether the DPF has a fault by detecting the particulate matter concentration, which is more accurate than judging the fault by the pressure difference. In addition, the present application detects the particulate matter concentration ratio between the two sides of the DPF, and then judges the type of DPF failure based on the particulate matter concentration ratio, which can eliminate the situation where the particulate matter emission exceeds the standard due to a fault of a non-DPF device, and avoid false fault reports.

Description

Technical Field

[0001] The present application relates to the field of engine aftertreatment technology, and in particular to a diagnostic method, device, engine, and vehicle for identifying DPF failure risks. Background Art

[0002] A diesel particulate filter (DPF) is an engine exhaust aftertreatment device used to reduce particulate matter in the exhaust. Its working principle is that its internal ceramic honeycomb structure or wall-flow filter device can collect particulate matter in the exhaust gas.

[0003] When particulate matter accumulates in the DPF to a certain level, it can cause a DPF blockage failure. At this point, the engine exhaust temperature needs to be increased to burn off the particulate matter and regenerate the DPF. During DPF regeneration, excessively high exhaust gas temperatures can burn through the DPF, causing a DPF burnout failure.

[0004] Emission regulations require that DPFs be able to detect both blockage and burnout failures. Existing DPF fault detection utilizes a pressure differential sensor and a particulate matter content sensor. The particulate matter content sensor measures the PM value at the DPF outlet, indicating an abnormal PM value if a burnout occurs. The pressure differential sensor measures the pressure difference between the DPF's inlet and outlet sides, indicating an abnormal pressure difference if a blockage occurs.

[0005] The prior art also has the following defects:

[0006] (1) The exhaust particulate matter content of the engine under different operating conditions is different, making it difficult to determine the exact PM abnormality.

[0007] (2) If both blockage and burn-through faults exist at the same time, the exhaust gas will be ejected from the burn-through hole to form turbulence, affecting the pressure sensor on the outlet side of the DPF, causing detection errors.

[0008] (3) When the excessive particulate matter content is caused by engine failure or poor fuel quality, the existing DPF fault detection method will identify it as a DPF fault.

[0009] Therefore, in order to accurately detect whether a blockage fault or a burn-through fault exists in the DPF device, the present application provides a diagnostic method for identifying the risk of DPF failure. Summary of the Invention

[0010] To overcome the problems existing in the related art, the first aspect of the present application provides a diagnostic method for identifying DPF failure risk, comprising:

[0011] S1. Obtaining a first particle matter concentration and a second particle matter concentration; the first particle matter concentration is used to detect the particle matter concentration on the air inlet side of the DPF device, and the second particle matter concentration is used to detect the particle matter concentration on the air outlet side of the DPF device;

[0012] S2. determining a particle matter concentration ratio according to the first particle matter concentration and the second particle matter concentration;

[0013] S3. Determining a fault type based on the particle concentration ratio and the second particle concentration; if the particle concentration ratio is greater than or equal to an upper particle concentration ratio limit, and the second particle concentration is less than or equal to a lower particle concentration limit, determining the fault type as a DPF blockage fault;

[0014] If the particle concentration ratio is less than a particle concentration ratio lower limit, and the second particle concentration is greater than a particle concentration upper limit, the fault type is determined to be a DPF burn-through fault.

[0015] In one embodiment, before obtaining the first particle matter concentration and the second particle matter concentration, the method further includes:

[0016] S101, obtaining current engine speed and torque;

[0017] S102, determining a theoretical particulate matter concentration ratio and a theoretical second particulate matter concentration according to the speed, the torque, and the engine emission model;

[0018] S103, determining an upper limit of a particle concentration ratio and a lower limit of a particle concentration ratio according to the theoretical particle concentration ratio;

[0019] S104: Determine a lower limit and an upper limit of a particle matter concentration according to the second particle matter concentration.

[0020] In one embodiment, before obtaining the current engine speed and torque, the method further includes:

[0021] Build an engine emissions model.

[0022] In one embodiment, constructing the engine emission model specifically includes:

[0023] Run the engine on a test bench at a preset torque and speed;

[0024] obtaining a first particulate matter concentration and a second particulate matter concentration of an engine;

[0025] determining a theoretical particulate matter concentration ratio under each operating condition according to the first particulate matter concentration and the second particulate matter concentration;

[0026] A fitting function of the theoretical particulate matter concentration ratio is obtained by fitting the theoretical particulate matter concentration ratio and the exhaust flow rate;

[0027] A fitting function of the theoretical second particulate matter concentration is obtained according to the theoretical second particulate matter concentration and the exhaust flow rate.

[0028] In one embodiment, the engine emission model is defined as:

[0029] q1=f1(T,W)

[0030] k1=f2(T,W)

[0031] Where f1(T, W) is the fitting function of the theoretical second particle concentration, f2(T, W) is the fitting function of the theoretical particle concentration ratio, T is the current engine torque, W is the current engine speed, q1 is the theoretical particle concentration under the current operating condition, and k1 is the theoretical particle concentration ratio under the current operating condition.

[0032] The second aspect of the present application provides a diagnostic device for identifying DPF failure risks, which is implemented based on the diagnostic method described in the first aspect of the present application.

[0033] A third aspect of the present application provides an engine comprising the diagnostic device described in the second aspect of the present application.

[0034] A fourth aspect of the present application provides a vehicle provided with the engine described in the third aspect of the present application.

[0035] The technical solution provided by this application may have the following beneficial effects:

[0036] This application installs particulate matter concentration sensors at the DPF's air inlet and outlet. This sensor detects particulate matter concentration to determine if the DPF is faulty, which is more accurate than determining faults based on pressure differences. Furthermore, this application detects the particulate matter concentration ratio between the two sides of the DPF and then determines the type of DPF fault based on this ratio. This can eliminate excessive particulate matter emissions caused by non-DPF device failures and avoid false fault reports.

[0037] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other objects, features and advantages of the present application will become more apparent through a more detailed description of exemplary embodiments of the present application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the present application.

[0039] Figure 1Schematic diagram of a flow chart of a diagnostic method for identifying DPF failure risk according to an embodiment of the present application;

[0040] Figure 2 Graph showing the relationship between the particle concentration ratios of a DPF blockage component and a DPF normal component shown in an embodiment of the present application;

[0041] Figure 3 This is a graph showing the relationship between the particulate matter concentration ratios of the DPF burn-through component and the DPF normal component shown in the embodiment of the present application. DETAILED DESCRIPTION

[0042] The preferred embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. Instead, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0043] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0044] It should be understood that although the terms "first", "second", "third", etc. may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0045] Example 1

[0046] In order to overcome the shortcomings of existing DPF fault detection methods, the present invention provides a diagnostic method for identifying DPF fault risks, such as Figure 1 As shown, the following steps are included:

[0047] S1. Obtaining a first particle matter concentration and a second particle matter concentration;

[0048] Specifically, the first particle concentration is used to detect the particle concentration on the DPF air inlet side, and the second particle concentration is used to detect the particle concentration on the DPF air outlet side.

[0049] In an embodiment of the present application, particulate matter sensors are respectively provided on the air inlet side and the air outlet side of the DPF of the engine exhaust duct.

[0050] S2. determining a particle matter concentration ratio according to the first particle matter concentration and the second particle matter concentration;

[0051] S3. Determine a fault type according to the particle matter concentration ratio and the second particle matter concentration.

[0052] In the case of a DPF blockage failure, the flow rate of exhaust gas passing through the DPF blockage component becomes smaller, causing the particle concentration ratio to increase sharply, and at the same time, the second particle concentration is lower than the particle concentration lower limit.

[0053] In the case of a DPF burn-through failure, exhaust gas leaks from the burn-through hole of the DPF burn-through component, causing the second particulate matter concentration to increase sharply, while the particulate matter concentration ratio is reduced to within 10.

[0054] Specifically, if the particle concentration ratio is greater than or equal to an upper particle concentration ratio limit, and the second particle concentration is less than or equal to a lower particle concentration limit, the fault type is determined to be a DPF blockage fault;

[0055] Specifically, if the particle concentration ratio is less than a particle concentration ratio lower limit, and the second particle concentration is greater than the particle concentration upper limit, the fault type is determined to be a DPF burn-through fault.

[0056] like Figure 2 and Figure 3 As shown, Figure 2 The blue line in the figure shows the relationship between the particle concentration ratio of the DPF blockage and the exhaust flow rate. Figure 2 The red line in the figure shows the relationship between the particulate matter concentration ratio and exhaust flow rate of a normal DPF. Figure 3 The blue line in the figure shows the relationship between the particle concentration ratio of the DPF burnout part and the exhaust flow rate. Figure 3 The red line in the figure shows the relationship between the particulate matter concentration ratio and exhaust flow rate of a normal DPF.

[0057] This embodiment of the present application installs particulate matter concentration sensors at the DPF's air inlet and outlet. This sensor detects particulate matter concentration to determine whether the DPF is faulty, which is more accurate than determining faults based on pressure differences. Furthermore, this embodiment detects the ratio of particulate matter concentrations on both sides of the DPF and determines the type of DPF fault based on this ratio. This can eliminate excessive particulate matter emissions caused by non-DPF device failures and avoid false fault alarms.

[0058] Example 2

[0059] Based on the first embodiment, the particulate matter concentration ratio is linearly correlated with the engine power. When the engine power increases, the concentration of particulate matter in the exhaust gas increases.

[0060] Therefore, when the engine and DPF are operating normally, the particle concentration ratio between the two sides of the DPF is related to the engine operating conditions, and the fault reporting accuracy using the threshold comparison method is low.

[0061] The present application also provides a diagnostic method for identifying DPF failure risks, such as Figure 1 As shown, the following steps are included:

[0062] S1. Obtaining a first particle matter concentration and a second particle matter concentration;

[0063] S2. determining a particle matter concentration ratio according to the first particle matter concentration and the second particle matter concentration;

[0064] S3, determining a fault type according to the particle concentration ratio and the second particle concentration,

[0065] If the particle concentration ratio is greater than or equal to the particle concentration ratio upper limit, and the second particle concentration is less than or equal to the particle concentration lower limit, determining the fault type as a DPF blockage fault;

[0066] If the particle concentration ratio is less than a particle concentration ratio lower limit, and the second particle concentration is greater than a particle concentration upper limit, the fault type is determined to be a DPF burn-through fault.

[0067] Furthermore, in S1, it specifically includes:

[0068] S101, obtaining current engine speed and torque;

[0069] S102, determining a theoretical particulate matter concentration ratio and a theoretical second particulate matter concentration according to the speed, the torque, and the engine emission model;

[0070] S103, determining an upper limit of a particle concentration ratio and a lower limit of a particle concentration ratio according to the theoretical particle concentration ratio;

[0071] S104: Determine a lower limit and an upper limit of a particle matter concentration according to the second particle matter concentration.

[0072] Specifically, the calculation formula for the upper limit and lower limit of the particle concentration ratio is:

[0073] q1=εq0

[0074] q2=ηq0

[0075] Among them, q1 is the upper limit of the particle concentration ratio, q0 is the theoretical second particle concentration, q2 is the lower limit of the particle concentration ratio, q0 is the theoretical second particle concentration, and ε and η are correction coefficients.

[0076] Specifically, the calculation formulas for the upper limit of the particle concentration ratio and the lower limit of the particle concentration ratio are:

[0077] k1=αk0

[0078] k2=βk0

[0079] Among them, k1 is the upper limit of the particle concentration ratio, k2 is the lower limit of the particle concentration ratio, k0 is the theoretical particle concentration ratio, and α and β are correction coefficients.

[0080] Furthermore, before S1, it also includes: building an engine emission model.

[0081] Specifically, the engine emission model is constructed including the following steps:

[0082] Run the engine on a test bench at a preset torque and speed;

[0083] obtaining a first particulate matter concentration and a second particulate matter concentration of an engine;

[0084] determining a theoretical particulate matter concentration ratio under each operating condition according to the first particulate matter concentration and the second particulate matter concentration;

[0085] A fitting function of the theoretical particulate matter concentration ratio is obtained by fitting the theoretical particulate matter concentration ratio and the exhaust flow rate;

[0086] A fitting function of the theoretical second particulate matter concentration is obtained according to the theoretical second particulate matter concentration and the exhaust flow rate.

[0087] Furthermore, the engine emission model is defined as:

[0088] q0=f1(T,W)

[0089] k0=f2(T,W)

[0090] Where f1(T,W) is the fitting function of the theoretical second particle concentration, f2(T,W) is the fitting function of the theoretical particle concentration ratio, T is the current engine torque, W is the current engine speed, q 01 is the theoretical particle concentration under the current working conditions, and k0 is the theoretical particle concentration ratio under the current working conditions.

[0091] In this embodiment of the present application, before performing DPF fault detection, engine speed and torque parameters are obtained and input into the engine emissions model to obtain a theoretical particulate matter concentration ratio and a theoretical particulate matter concentration. Under different engine operating conditions, the theoretical particulate matter concentration ratio and the theoretical particulate matter concentration are calculated to obtain dynamic thresholds, and the DPF fault type is determined by comparing the dynamic thresholds.

[0092] Example 3

[0093] A diagnostic device for identifying DPF failure risks is implemented based on the diagnostic method described in the first or second embodiment.

[0094] Example 4

[0095] An engine includes the diagnostic device described in embodiment three.

[0096] Example 5

[0097] A vehicle is provided with the engine described in the fourth embodiment.

[0098] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0099] The solution of the present application has been described in detail above with reference to the accompanying drawings. In the above embodiments, the description of each embodiment has its own focus. For parts not described in detail in a particular embodiment, please refer to the relevant description of other embodiments. Those skilled in the art should also be aware that the actions and modules mentioned in the description are not necessarily required for this application.

[0100] In addition, it can be understood that the steps in the method of the embodiment of the present application can be adjusted in order, merged and deleted according to actual needs, and the modules in the device of the embodiment of the present application can be merged, divided and deleted according to actual needs.

[0101] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0102] Alternatively, the present application can also be implemented as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) on which executable code (or computer program, or computer instruction code) is stored. When the executable code (or computer program, or computer instruction code) is executed by a processor of an electronic device (or electronic device, server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0103] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the application herein may be implemented as electronic hardware, computer software, or combinations of both.

[0104] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems and methods according to multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0105] The embodiments of the present application have been described above. The above description is illustrative and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or improvements to the technology in the market, or to enable other persons skilled in the art to understand the embodiments disclosed herein.

Claims

1. A diagnostic method for identifying DPF failure risk, characterized in that: include: S1. Obtaining a first particle matter concentration and a second particle matter concentration; the first particle matter concentration is used to detect the particle matter concentration on the air inlet side of the DPF device, and the second particle matter concentration is used to detect the particle matter concentration on the air outlet side of the DPF device; S2. determining a particle matter concentration ratio according to the first particle matter concentration and the second particle matter concentration; S3. Determining a fault type based on the particle concentration ratio and the second particle concentration; if the particle concentration ratio is greater than or equal to an upper particle concentration ratio limit, and the second particle concentration is less than or equal to a lower particle concentration limit, determining the fault type as a DPF blockage fault; If the particle concentration ratio is less than a particle concentration ratio lower limit, and the second particle concentration is greater than a particle concentration upper limit, determining the fault type as a DPF burn-through fault; Before obtaining the first particle matter concentration and the second particle matter concentration, the method further includes: S101, obtaining current engine speed and torque; S102, determining a theoretical particulate matter concentration ratio and a theoretical second particulate matter concentration according to the speed, the torque, and an engine emission model; S103, determining an upper limit of a particle concentration ratio and a lower limit of a particle concentration ratio according to the theoretical particle concentration ratio; S104, determining a lower limit and an upper limit of a particulate matter concentration according to the second particulate matter concentration; Before obtaining the current engine speed and torque, the method further includes: Build engine emission models; The engine emission model is defined as: in, is the fitting function of the theoretical second particle concentration, is the fitting function of the theoretical particle concentration ratio, is the current engine torque, is the current engine speed, is the theoretical particulate matter concentration under the current working conditions, is the theoretical particle concentration ratio under the current working conditions.

2. A diagnostic method for identifying DPF failure risk according to claim 1, characterized in that: The engine emission model is constructed, specifically comprising: Run the engine on a test bench at a preset torque and speed; obtaining a first particulate matter concentration and a second particulate matter concentration of an engine; determining a theoretical particulate matter concentration ratio under each operating condition according to the first particulate matter concentration and the second particulate matter concentration; A fitting function of the theoretical particulate matter concentration ratio is obtained by fitting the theoretical particulate matter concentration ratio and the exhaust flow rate; A fitting function of the theoretical second particulate matter concentration is obtained according to the theoretical second particulate matter concentration and the exhaust flow rate.

3. A diagnostic device for identifying DPF failure risk, characterized in that: The method is implemented based on any one of claims 1 to 2.

4. An engine, characterized in that: The diagnostic device according to claim 3 is included.

5. A vehicle, characterized in that: The engine according to claim 4 is provided.

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