Diagnosis method and device for recognizing DPF fault risk, engine and vehicle

Abstract

The invention relates to the technical field of engine aftertreatment, in particular to a diagnosis method and device for recognizing DPF fault risks, an engine and a vehicle. The method comprises the steps that S1, the first particulate matter concentration and the second particulate matter concentration are obtained; s2, determining a particulate matter concentration ratio according to the first particulate matter concentration and the second particulate matter concentration; and S3, determining a fault type according to the particulate matter concentration ratio and the second particulate matter concentration. The particulate matter concentration sensors are arranged at the air inlet and the air outlet of the DPF, whether the DPF breaks down or not is determined by detecting the particulate matter concentration, and fault judgment is more accurate compared with fault judgment through pressure difference. And moreover, the particulate matter concentration ratio between the two sides of the DPF is detected, and then the fault type of the DPF is judged according to the particulate matter concentration ratio, so that the situation that particulate matter emission exceeds the standard due to the fault of a non-DPF device can be eliminated, and wrong fault reporting is avoided.

Description

Technical Field

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

[0002] Diesel Particulate Filter (DPF) is an engine exhaust aftertreatment device used to reduce the content of exhaust particulate matter. Its working principle is that the ceramic honeycomb structure or wall flow filter device set inside it can collect particulate matter in the exhaust gas.

[0003] When the particulate matter in the DPF accumulates to a certain extent, it will cause DPF blockage failure. At this time, it is necessary to increase the engine exhaust temperature to burn off the particulate matter in the DPF and achieve DPF regeneration. When the DPF is regenerated, the excessively high exhaust gas temperature will burn through the DPF, causing DPF burn-through failure.

[0004] According to emission regulations, DPF needs to be able to detect blockage and burn-through faults. Existing DPF fault detection uses a pressure difference sensor and a particulate matter content sensor to achieve fault detection. Among them, the particulate matter content sensor is used to detect the PM value on the outlet side of the DPF. If there is a burn-through fault, the PM value is abnormal; the pressure difference sensor is used to detect the pressure difference between the intake and outlet sides of the DPF. If there is a blockage fault, the pressure difference value is abnormal.

[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 there is a blockage fault or a burn-through fault in the DPF device, the present application provides a diagnostic method for identifying the risk of DPF failure. Summary of the invention

[0010] In order 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 concentration and a second particle concentration; the first particle concentration is used to detect the particle concentration on the air inlet side of the DPF device, and the second particle concentration is used to detect the particle 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 according to the particle concentration ratio and the second particle concentration, and if the particle concentration ratio is greater than or equal to an upper limit of the particle concentration ratio, and the second particle concentration is less than or equal to a lower limit of the particle concentration, 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 concentration and the second particle 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 rotation 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 implementation, 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 at preset torque and speed on a test bench;

[0024] Obtaining a first particle concentration and a second particle concentration of an engine;

[0025] Determining a theoretical particle concentration ratio under each operating condition according to the first particle concentration and the second particle concentration;

[0026] According to the theoretical particle concentration ratio and the exhaust flow rate, a fitting function of the theoretical particle concentration ratio is obtained;

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

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

[0029] q 1 =f 1 (T,W)

[0030] k 1 =f 2 (T,W)

[0031] Among them, f 1 (T,W) is the fitting function of the theoretical second particle concentration, f 2 (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 1 is the theoretical particle concentration under the current working conditions, k 1 is the theoretical particle concentration ratio under the current working conditions.

[0032] A 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] The present application sets a particle concentration sensor at the air inlet and outlet of the DPF, and determines whether the DPF is faulty by detecting the particle concentration, which is more accurate than judging the fault by the pressure difference. In addition, the present application detects the particle concentration ratio between the two sides of the DPF, and then judges the type of DPF fault according to the particle concentration ratio, which can exclude the situation of excessive particle emissions caused by non-DPF device faults 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 1 A schematic flow chart of a diagnostic method for identifying DPF failure risk shown in an embodiment of the present application;

[0040] Figure 2 It is a relationship diagram of the particle concentration ratio of the DPF blocked part and the DPF normal part shown in the embodiment of the present application;

[0041] Figure 3 This is a relationship diagram of the particle concentration ratio of the DPF burn-through part and the DPF normal part 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. On the contrary, 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. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more 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 the same type of information 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, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0045] Embodiment 1

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

[0047] S1. Obtaining a first particle concentration and a second particle 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 the embodiment of the present application, particulate matter sensors are respectively arranged on the air inlet side and the air outlet side of the DPF of the engine exhaust pipe.

[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 less than the particle concentration lower limit.

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

[0054] Specifically, 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, the fault type is determined to be a DPF blockage fault;

[0055] Specifically, if the particle matter concentration ratio is less than a particle matter concentration ratio lower limit, and the second particle matter concentration is greater than the particle matter 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 particle 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 particle concentration ratio and exhaust flow rate of a normal DPF.

[0057] The embodiment of the present application sets a particle concentration sensor at the air inlet and outlet of the DPF, and determines whether the DPF is faulty by detecting the particle concentration, which is more accurate than judging the fault by the pressure difference. In addition, the embodiment of the present application detects the particle concentration ratio between the two sides of the DPF, and judges the type of DPF fault according to the particle concentration ratio, which can exclude the situation of excessive particle emissions caused by non-DPF device faults and avoid false fault reports.

[0058] Embodiment 2

[0059] Based on the first embodiment, the particle concentration ratio is linearly correlated with the engine power. When the engine power increases, the particle concentration 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 risk, such as Figure 1 As shown, the following steps are included:

[0062] S1. Obtaining a first particle concentration and a second particle 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, the fault type is determined to be 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 rotation 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 of the particle concentration ratio and the lower limit of the particle concentration ratio is:

[0073] q 1 =εq 0

[0074] q 2 =ηq 0

[0075] Among them, q 1 is the upper limit of the particle concentration ratio, q 0 is the theoretical second particle concentration, q 2 is the lower limit of the particle concentration ratio, q 0 is the theoretical second particle concentration, 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] k 1 =αk 0

[0078] k 2 =βk 0

[0079] Among them, k 1 is the upper limit of the particle concentration ratio, k 2 is the lower limit of the particle concentration ratio, k 0 is the theoretical particle concentration ratio, α and β are correction coefficients.

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

[0081] Specifically, the construction of the engine emission model includes the following steps:

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

[0083] Obtaining a first particle concentration and a second particle concentration of an engine;

[0084] Determining a theoretical particle concentration ratio under each operating condition according to the first particle concentration and the second particle concentration;

[0085] According to the theoretical particle concentration ratio and the exhaust flow rate, a fitting function of the theoretical particle concentration ratio is obtained;

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

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

[0088] q 0 =f 1 (T,W)

[0089] k 0 =f 2 (T,W)

[0090] Among them, f 1 (T,W) is the fitting function of the theoretical second particle concentration, f 2 (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, k 0 is the theoretical particle concentration ratio under the current working conditions.

[0091] In the embodiment of the present application, before performing DPF fault detection, the speed and torque parameters of the engine are obtained, and the speed and torque are input into the engine emission model to obtain the theoretical particle concentration ratio and the theoretical particle concentration. Under different working conditions of the engine, the theoretical particle concentration ratio and the theoretical particle concentration are calculated to obtain a dynamic threshold value, and the DPF fault type is determined by comparing the dynamic threshold value.

[0092] Embodiment 3

[0093] A diagnostic device for identifying DPF failure risk is implemented based on the diagnostic method described in Example 1 or Example 2.

[0094] Embodiment 4

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

[0096] Embodiment 5

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

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

[0099] The scheme 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 emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. Those skilled in the art should also be aware that the actions and modules involved in the description are not necessarily required for the present 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, combined and deleted according to actual needs, and the modules in the device of the embodiment of the present application can be combined, 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-temporary 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 chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system and method according to multiple embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a part of a module, a program segment or a code, and the part of the module, the program segment or the 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 square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square 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 square box in the block diagram and / or the flow chart, and the combination of the square boxes in the block diagram and / or the 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, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill 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 concentration and a second particle concentration; the first particle concentration is used to detect the particle concentration on the air inlet side of the DPF device, and the second particle concentration is used to detect the particle 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 according to the particle concentration ratio and the second particle concentration, and if the particle concentration ratio is greater than or equal to an upper limit of the particle concentration ratio, and the second particle concentration is less than or equal to a lower limit of the particle concentration, 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, the fault type is determined to be a DPF burn-through fault.

2. A diagnostic method for identifying DPF failure risk according to claim 1, characterized in that: Before obtaining the first particle concentration and the second particle 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 rotation speed, the torque and the 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: Determine a lower limit and an upper limit of a particle matter concentration according to the second particle matter concentration.

3. A diagnostic method for identifying DPF failure risk according to claim 2, characterized in that: Before obtaining the current engine speed and torque, the method further includes: Build an engine emissions model.

4. A diagnostic method for identifying DPF failure risk according to claim 3, characterized in that: The constructing of the engine emission model specifically includes: Run the engine at preset torque and speed on a test bench; Obtaining a first particle concentration and a second particle concentration of an engine; Determining a theoretical particle concentration ratio under each operating condition according to the first particle concentration and the second particle concentration; According to the theoretical particle concentration ratio and the exhaust flow rate, a fitting function of the theoretical particle concentration ratio is obtained; A fitting function of the theoretical second particulate matter concentration is obtained by fitting the theoretical second particulate matter concentration and the exhaust flow rate.

5. A diagnostic method for identifying DPF failure risk according to claim 3, characterized in that: The engine emission model is defined as: q1=f1(T,W) k1=f2(T,W) Among them, 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 working condition, and k1 is the theoretical particle concentration ratio under the current working condition.

6. A diagnostic device for identifying DPF failure risk, characterized in that: The method is implemented based on any one of claims 1 to 5.

7. An engine, characterized in that: Comprising the diagnostic device according to claim 6.

8. A vehicle, characterized in that: An engine according to claim 7 is provided.

Images