Exhaust pipe and aftertreatment blowby identification method and system, storage medium, engine

Abstract

The application provides an exhaust pipe and a post-processing gas leakage identification method and system, a storage medium and an engine. The exhaust pipe and the post-processing gas leakage identification method comprise the following steps: acquiring DPF inlet and outlet pressure data; acquiring post-processing system pressure data; establishing a post-processing system pressure reference model according to the DPF inlet and outlet pressure data; correcting the post-processing system pressure reference model through characteristic parameters of the post-processing system to obtain a DPF inlet flow resistance reference and a DPF outlet flow resistance reference; acquiring a first window average value of DPF inlet pressure flow resistance and a second window average value of DPF outlet pressure flow resistance in real time; and deriving an identification result according to the DPF inlet flow resistance reference, the first window average value, the DPF outlet flow resistance reference and the second window average value. According to the technical scheme of the application, the post-processing system pressure reference model is established according to the DPF inlet and outlet pressure data of the engine network, which is more direct and accurate, is suitable for various vehicle models, and simplifies the calibration test workload.

Description

Technical Field

[0001] This application relates to the field of engine technology, and more specifically, to a method and system for identifying exhaust pipe and aftertreatment leaks, a storage medium, and an engine. Background Technology

[0002] Due to the stringent requirements of diesel engine emission regulations, existing diesel engines primarily employ a DOC + DPF + SCR technology. The stringent requirements for emissions and OBD (On-Board Diagnostics) place high demands on the conversion efficiency and durability of the aftertreatment system. Since the aftertreatment system comprises multiple modules and components, it carries the risk of leakage in practical use. If a leak occurs in the exhaust pipe or aftertreatment system, the conversion efficiency will decrease, DPF regeneration will easily fail, SCR will easily crystallize, and leaks can also easily damage the aftertreatment wiring harness sensors. Therefore, exhaust system leaks can lead to serious consequences such as aftertreatment failure and OBD alarms. Thus, it is essential to develop real-time exhaust system leak diagnostic technology to promptly guide drivers and service personnel in identifying leaks, protecting the aftertreatment system, and avoiding serious consequences.

[0003] Currently, a common approach is to use the readings of a DPF differential pressure sensor to calculate the actual exhaust back pressure, and then compare this actual exhaust back pressure with the theoretical exhaust back pressure. If the difference is large, it is determined that there is a leak in the exhaust pipe or aftertreatment system. However, the theoretical exhaust back pressure is affected by many factors, and many patents do not consider these factors, resulting in inaccurate diagnosis. Summary of the Invention

[0004] This application aims to solve or improve the aforementioned technical problems.

[0005] Therefore, the primary objective of this application is to provide a method for identifying exhaust pipe and aftertreatment leaks.

[0006] The second objective of this application is to provide an exhaust pipe and aftertreatment leak detection system.

[0007] A third objective of this application is to provide an exhaust pipe and aftertreatment leak detection system.

[0008] The fourth objective of this application is to provide a readable storage medium.

[0009] The fifth objective of this application is to provide an engine.

[0010] To achieve the first objective of this application, the technical solution of the first aspect of this application provides a method for identifying exhaust pipe and aftertreatment leakage, comprising: acquiring DPF inlet and outlet pressure data; acquiring aftertreatment system pressure data; establishing an aftertreatment system pressure reference model based on the DPF inlet and outlet pressure data; correcting the aftertreatment system pressure reference model through characteristic parameters of the aftertreatment system to obtain DPF inlet flow resistance reference and DPF outlet flow resistance reference; acquiring the first window average value of DPF inlet pressure flow resistance and the second window average value of DPF outlet pressure flow resistance in real time; and obtaining the identification result based on the DPF inlet flow resistance reference, the first window average value, the DPF outlet flow resistance reference, and the second window average value.

[0011] According to the exhaust pipe and aftertreatment leakage identification method provided in this application, the following steps are first taken: First, DPF inlet and outlet pressure data and aftertreatment system pressure data under normal conditions are acquired from the vehicle network. The DPF inlet and outlet pressure data are based on big data from the engine network. The aftertreatment system pressure data meets the set limits. Then, a pressure benchmark model for the aftertreatment system under normal conditions is established based on the DPF inlet and outlet pressure data. The pressure benchmark model is then corrected using the characteristic parameters of the aftertreatment system to obtain the DPF inlet flow resistance benchmark and the DPF outlet flow resistance benchmark. Then, the first window average value of the DPF inlet pressure flow resistance and the second window average value of the DPF outlet pressure flow resistance are collected in real time. Finally, the exhaust pipe and aftertreatment leakage identification results are obtained based on the DPF inlet and outlet pressure data, the first window average value, the DPF outlet flow resistance benchmark, and the second window average value. Establishing a pressure benchmark model for the aftertreatment system under normal conditions based on the DPF inlet and outlet pressure data from the engine network is more direct and accurate, applicable to various vehicle models, and simplifies calibration testing workload. Furthermore, the use of a window average value algorithm in the DPF inlet and outlet pressure readings avoids misjudgments caused by fluctuations.

[0012] In addition, the technical solution provided in this application may also have the following additional technical features:

[0013] In the above technical solution, the pressure data of the aftertreatment system includes one or a combination of the following: engine speed, torque, exhaust flow rate, exhaust temperature, DPF inlet pressure sensor reading, DPF outlet pressure sensor reading, time since last regeneration, DPF carbon load, total engine running time, mileage, and DPF ash content.

[0014] In this technical solution, the aftertreatment system pressure data under normal conditions includes engine speed, torque, exhaust flow rate, exhaust temperature, DPF inlet pressure sensor reading, DPF outlet pressure sensor reading, time since last regeneration, DPF carbon load, total engine running time, mileage, and DPF ash content. All aftertreatment system pressure data meet the set limits.

[0015] In the above technical solution, the pressure reference model of the aftertreatment system is corrected by the characteristic parameters of the aftertreatment system to obtain the DPF inlet flow resistance reference and the DPF outlet flow resistance reference. Specifically, this includes obtaining the characteristic parameters within 2 hours after the first successful regeneration based on the pressure data of the aftertreatment system. The characteristic parameters include: exhaust temperature, exhaust flow rate, DPF inlet pressure sensor reading, DPF outlet pressure sensor reading, DPF inlet flow resistance, average value of the inlet flow resistance window, DPF outlet flow resistance, average value of the outlet flow resistance window, DPF ash content, and DPF carbon loading. The pressure reference model of the aftertreatment system is corrected by the characteristic parameters to obtain the DPF inlet flow resistance reference and the DPF outlet flow resistance reference.

[0016] In this technical solution, the pressure reference model of the aftertreatment system is corrected using characteristic parameters of the aftertreatment system to obtain the DPF inlet flow resistance reference and the DPF outlet flow resistance reference. Specifically, the pressure reference model is corrected based on exhaust temperature, exhaust flow rate, DPF inlet pressure sensor readings, DPF outlet pressure sensor readings, DPF inlet flow resistance, average inlet flow resistance window value, DPF outlet flow resistance, average outlet flow resistance window value, DPF ash content, and DPF carbon loading within two hours after the first successful regeneration. In other words, the aftertreatment system pressure reference model is a reference model composed of corrected exhaust temperature, exhaust flow rate, DPF inlet pressure sensor readings, DPF outlet pressure sensor readings, DPF inlet flow resistance and average window value, DPF outlet flow resistance and average window value, DPF ash content, and DPF carbon loading within two hours after the first successful regeneration. This reference model represents the DPF inlet flow resistance and DPF outlet flow resistance reference under normal exhaust system conditions.

[0017] In the above technical solution, the identification result is obtained based on the DPF inlet flow resistance reference, the average value of the first window, the DPF outlet flow resistance reference, and the average value of the second window. Specifically, this includes: obtaining the inlet pressure error based on the DPF inlet flow resistance reference and the average value of the first window; determining whether the inlet pressure error is greater than the first set value; if so, the identification result is that the DPF front exhaust pipe is leaking.

[0018] In this technical solution, the identification result is obtained based on the DPF inlet flow resistance reference, the average value of the first window, the DPF outlet flow resistance reference, and the average value of the second window. Specifically, the inlet pressure error is first calculated based on the DPF inlet flow resistance reference and the average value of the first window. The inlet pressure error is used to diagnose exhaust system leaks. If the inlet pressure error is greater than a first set value, a leak in the DPF front exhaust pipe is diagnosed.

[0019] The above technical solution, which derives the identification result based on the DPF inlet flow resistance reference, the average value of the first window, the DPF outlet flow resistance reference, and the average value of the second window, also includes: deriving the outlet pressure error based on the DPF outlet flow resistance reference and the average value of the second window; determining whether the outlet pressure error is greater than a second set value; if so, the identification result is that the exhaust pipe from the DPF to the SCR section is leaking.

[0020] In this technical solution, the identification result is derived based on the DPF inlet flow resistance reference, the average value of the first window, the DPF outlet flow resistance reference, and the average value of the second window. It also includes deriving the outlet pressure error based on the DPF outlet flow resistance reference and the average value of the second window. If the outlet pressure error exceeds a second set value, a leak in the exhaust pipe from the DPF to the SCR section is diagnosed.

[0021] In the above technical solution, obtaining inlet and outlet pressure data specifically includes: obtaining initial DPF inlet and outlet pressure data of the vehicle network; preprocessing the initial DPF inlet and outlet pressure data to obtain DPF inlet and outlet pressure data.

[0022] In this technical solution, the DPF inlet and outlet pressure data of the vehicle network is obtained. Specifically, the initial DPF inlet and outlet pressure data of the vehicle network is first obtained. Then, the initial DPF inlet and outlet pressure data is preprocessed to obtain the DPF inlet and outlet pressure data. It can be understood that by cleaning the initial DPF inlet and outlet pressure data, some outliers can be removed, making the identification results more direct and accurate.

[0023] To achieve the second objective of this application, the technical solution of the second aspect of this application provides an exhaust pipe and aftertreatment leakage identification system, comprising: a first acquisition module for acquiring DPF inlet and outlet pressure data from a vehicle network; a second acquisition module for acquiring aftertreatment system pressure data under normal conditions; a model building module for establishing an aftertreatment system pressure benchmark model based on the DPF inlet and outlet pressure data; a model correction module for correcting the aftertreatment system pressure benchmark model using the aftertreatment system pressure data to obtain a DPF inlet flow resistance benchmark and a DPF outlet flow resistance benchmark; a third acquisition module for acquiring the first window average value of the DPF inlet pressure flow resistance and the second window average value of the DPF outlet pressure flow resistance in real time; and an identification module for obtaining an identification result based on the DPF inlet flow resistance benchmark, the first window average value, the DPF outlet flow resistance benchmark, and the second window average value.

[0024] The exhaust pipe and aftertreatment leakage identification system provided in this application includes a first acquisition module, a second acquisition module, a model building module, a model correction module, a third acquisition module, and an identification module. The first acquisition module acquires DPF inlet and outlet pressure data from the vehicle network. The second acquisition module acquires aftertreatment system pressure data under normal conditions. The model building module establishes an aftertreatment system pressure benchmark model based on the DPF inlet and outlet pressure data. The model correction module corrects the aftertreatment system pressure benchmark model using the aftertreatment system pressure data to obtain DPF inlet flow resistance benchmarks and DPF outlet flow resistance benchmarks. The third acquisition module acquires the first window average value of the DPF inlet pressure flow resistance and the second window average value of the DPF outlet pressure flow resistance in real time. The identification module derives the identification result based on the DPF inlet flow resistance benchmark, the first window average value, the DPF outlet flow resistance benchmark, and the second window average value. Establishing an aftertreatment system pressure benchmark model under normal conditions based on DPF inlet and outlet pressure data from the engine network is more direct and accurate, applicable to various vehicle models, and simplifies calibration testing workload. Furthermore, the use of a window average value algorithm in the DPF inlet and outlet pressure readings avoids misjudgments caused by fluctuations.

[0025] To achieve the third objective of this application, the technical solution of the third aspect of this application provides an exhaust pipe and aftertreatment leakage identification system, including: a memory and a processor, wherein the memory stores a program or instructions that can be run on the processor, and when the processor executes the program or instructions, it implements the exhaust pipe and aftertreatment leakage identification method of any one of the technical solutions of the first aspect, and thus has the technical effects of any one of the technical solutions of the first aspect, which will not be elaborated here.

[0026] To achieve the fourth objective of this application, the technical solution of the fourth aspect of this application provides a readable storage medium storing a program or instructions thereon. When the program or instructions are executed by a processor, they implement the steps of the exhaust pipe and after-treatment leakage identification method of any one of the technical solutions of the first aspect, and thus have the technical effects of any one of the technical solutions of the first aspect, which will not be repeated here.

[0027] To achieve the fifth objective of this application, the technical solution of the fifth aspect of this application provides an engine, including: an exhaust pipe and an aftertreatment leak detection system as described in any of the technical solutions of the second aspect of this application; and / or an exhaust pipe and an aftertreatment leak detection system as described in any of the technical solutions of the third aspect of this application; and / or a readable storage medium as described in any of the technical solutions of the fourth aspect of this application.

[0028] The engine provided by the technical solution of this application includes an exhaust pipe and aftertreatment leak detection system as described in any of the technical solutions of the second aspect of this application, or an exhaust pipe and aftertreatment leak detection system as described in any of the technical solutions of the third aspect of this application, or a readable storage medium as described in any of the technical solutions of the fourth aspect of this application. Therefore, it has all the beneficial effects of the exhaust pipe and aftertreatment leak detection system as described in any of the technical solutions of the second aspect of this application, or an exhaust pipe and aftertreatment leak detection system as described in any of the technical solutions of the third aspect of this application, or a readable storage medium as described in any of the technical solutions of the fourth aspect of this application, which will not be elaborated here.

[0029] Additional aspects and advantages of this application will become apparent in the following description or may be learned by practice of this application. Attached Figure Description

[0030] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0031] Figure 1 This is a schematic flowchart illustrating the steps of an exhaust pipe and aftertreatment leak detection method according to an embodiment of this application.

[0032] Figure 2 This is a schematic flowchart illustrating the steps of an exhaust pipe and aftertreatment leak detection method according to an embodiment of this application.

[0033] Figure 3 This is a schematic flowchart illustrating the steps of an exhaust pipe and aftertreatment leak detection method according to an embodiment of this application.

[0034] Figure 4 This is a schematic flowchart illustrating the steps of an exhaust pipe and aftertreatment leak detection method according to an embodiment of this application.

[0035] Figure 5 This is a schematic flowchart illustrating the steps of an exhaust pipe and aftertreatment leak detection method according to an embodiment of this application.

[0036] Figure 6 This is a schematic block diagram of the structure of an exhaust pipe and aftertreatment leak detection system according to an embodiment of this application;

[0037] Figure 7 This is a schematic block diagram of the exhaust pipe and aftertreatment leak detection system according to another embodiment of this application;

[0038] Figure 8 This is a flowchart illustrating the steps of an exhaust pipe and aftertreatment leak detection method according to an embodiment of this application.

[0039] in, Figure 6 and Figure 7The correspondence between the reference numerals and component names in the attached drawings is as follows:

[0040] 10: Exhaust pipe and aftertreatment leak detection system; 110: First acquisition module; 120: Second acquisition module; 130: Model building module; 140: Model correction module; 150: Third acquisition module; 160: Recognition module; 20: Exhaust pipe and aftertreatment leak detection system; 300: Memory; 400: Processor. Detailed Implementation

[0041] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0042] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of this application is not limited to the specific embodiments disclosed below.

[0043] The following reference Figures 1 to 8 This application describes exhaust pipe and aftertreatment leak detection methods and systems, storage media, and engines according to some embodiments.

[0044] like Figure 1 As shown, an embodiment of the first aspect of this application provides a method for identifying exhaust pipe and aftertreatment leaks, comprising the following steps:

[0045] Step S102: Obtain DPF inlet and outlet pressure data;

[0046] Step S104: Obtain post-processing system pressure data;

[0047] Step S106: Establish a pressure reference model for the aftertreatment system based on the DPF inlet and outlet pressure data;

[0048] Step S108: Correct the pressure reference model of the post-treatment system using the characteristic parameters of the post-treatment system to obtain the DPF inlet flow resistance reference and the DPF outlet flow resistance reference.

[0049] Step S110: Real-time acquisition of the first window average value of the DPF inlet pressure flow resistance and the second window average value of the DPF outlet pressure flow resistance;

[0050] Step S112: Obtain the identification result based on the DPF inlet flow resistance reference, the average value of the first window, the DPF outlet flow resistance reference, and the average value of the second window.

[0051] According to the exhaust pipe and aftertreatment leakage identification method provided in this embodiment, the following steps are first taken: First, the DPF inlet and outlet pressure data and the aftertreatment system pressure data under normal conditions are acquired from the vehicle network. The DPF inlet and outlet pressure data are based on big data from the engine network. The aftertreatment system pressure data meets the set limits. Then, a baseline model of the aftertreatment system pressure under normal conditions is established based on the DPF inlet and outlet pressure data. The baseline model is then corrected using the characteristic parameters of the aftertreatment system to obtain the DPF inlet flow resistance baseline and the DPF outlet flow resistance baseline. Then, the first window average value of the DPF inlet pressure flow resistance and the second window average value of the DPF outlet pressure flow resistance are collected in real time. Finally, the exhaust pipe and aftertreatment leakage identification results are obtained based on the DPF inlet and outlet pressure data, the first window average value, the DPF outlet flow resistance baseline, and the second window average value. Establishing a baseline model of the aftertreatment system pressure under normal conditions based on the DPF inlet and outlet pressure data from the engine network is more direct and accurate, applicable to various vehicle models, and simplifies calibration testing workload. Furthermore, the use of a window average value algorithm in the DPF inlet and outlet pressure readings avoids misjudgments caused by fluctuations.

[0052] DPF stands for Diesel Particulate Filter.

[0053] In the above embodiments, the aftertreatment system pressure data includes engine speed, torque, exhaust flow rate, exhaust temperature, DPF inlet pressure sensor reading, DPF outlet pressure sensor reading, time since last regeneration, DPF carbon load, total engine running time, mileage, and DPF ash content. All aftertreatment system pressure data meet the set limits.

[0054] like Figure 2 As shown, according to an embodiment of the exhaust pipe and aftertreatment leakage identification method proposed in this application, the pressure reference model of the aftertreatment system is corrected by the characteristic parameters of the aftertreatment system to obtain the DPF inlet flow resistance reference and the DPF outlet flow resistance reference. Specifically, the method includes the following steps:

[0055] Step S202: Based on the pressure data of the aftertreatment system, obtain the characteristic parameters within 2 hours after the first successful regeneration. The characteristic parameters include: exhaust temperature, exhaust flow rate, DPF inlet pressure sensor reading, DPF outlet pressure sensor reading, DPF inlet flow resistance, average value of inlet flow resistance window, DPF outlet flow resistance, average value of outlet flow resistance window, DPF ash content, and DPF carbon loading.

[0056] Step S204: Correct the pressure reference model of the aftertreatment system through characteristic parameters to obtain the DPF inlet flow resistance reference and the DPF outlet flow resistance reference.

[0057] In this embodiment, the pressure reference model of the aftertreatment system is corrected using characteristic parameters of the aftertreatment system to obtain the DPF inlet flow resistance reference and the DPF outlet flow resistance reference. Specifically, the pressure reference model is corrected based on exhaust temperature, exhaust flow rate, DPF inlet pressure sensor readings, DPF outlet pressure sensor readings, DPF inlet flow resistance, average inlet flow resistance window value, DPF outlet flow resistance, average outlet flow resistance window value, DPF ash content, and DPF carbon loading within 2 hours after the first successful regeneration. It can be understood that the aftertreatment system pressure reference model is a reference model composed of the exhaust temperature, exhaust flow rate, DPF inlet pressure sensor readings, DPF outlet pressure sensor readings, DPF inlet flow resistance and average window value, DPF outlet flow resistance and average window value, DPF ash content, and DPF carbon loading corrected within 2 hours after the first successful regeneration. This reference model represents the DPF inlet flow resistance and DPF outlet flow resistance reference under normal exhaust system conditions.

[0058] like Figure 3 As shown, an exhaust pipe and aftertreatment leakage identification method according to an embodiment of this application derives the identification result based on the DPF inlet flow resistance reference, the average value of the first window, the DPF outlet flow resistance reference, and the average value of the second window. The method specifically includes the following steps:

[0059] Step S302: Calculate the inlet pressure error based on the DPF inlet flow resistance reference and the average value of the first window;

[0060] Step S304: Determine whether the inlet pressure error is greater than the first set value. If yes, proceed to step S306; otherwise, return to step S302.

[0061] Step S306: The identification result is that there is air leakage in the exhaust pipe of the DPF front section.

[0062] In this embodiment, the identification result is obtained based on the DPF inlet flow resistance reference, the average value of the first window, the DPF outlet flow resistance reference, and the average value of the second window. Specifically, the inlet pressure error is first obtained based on the DPF inlet flow resistance reference and the average value of the first window. The inlet pressure error is used to diagnose exhaust system leaks. If the inlet pressure error is greater than a first set value, a leak in the DPF front exhaust pipe is diagnosed.

[0063] like Figure 4 As shown, the exhaust pipe and aftertreatment leakage identification method according to an embodiment of this application derives the identification result based on the DPF inlet flow resistance reference, the average value of the first window, the DPF outlet flow resistance reference, and the average value of the second window, and further includes the following steps:

[0064] Step S402: Calculate the outlet pressure error based on the DPF outlet flow resistance reference and the average value of the second window;

[0065] Step S404: Determine whether the outlet pressure error is greater than the second set value. If yes, proceed to step S406; otherwise, return to step S402.

[0066] Step S406: The identification result is that there is air leakage in the exhaust pipe from DPF to SCR section.

[0067] In this embodiment, the identification result is derived based on the DPF inlet flow resistance reference, the average value of the first window, the DPF outlet flow resistance reference, and the average value of the second window. It also includes deriving the outlet pressure error based on the DPF outlet flow resistance reference and the average value of the second window. If the outlet pressure error is greater than a second set value, a leak in the exhaust pipe from the DPF to the SCR section is diagnosed.

[0068] SCR stands for Selective Catalytic Reduction.

[0069] like Figure 5 As shown, according to an embodiment of the exhaust pipe and aftertreatment leakage identification method proposed in this application, the method for obtaining inlet and outlet pressure data specifically includes the following steps:

[0070] Step S502: Obtain initial DPF inlet and outlet pressure data for the vehicle network;

[0071] Step S504: Preprocess the initial DPF inlet and outlet pressure data to obtain DPF inlet and outlet pressure data.

[0072] In this embodiment, the DPF inlet and outlet pressure data of the vehicle network is obtained, specifically by first acquiring the initial DPF inlet and outlet pressure data of the vehicle network. Then, the initial DPF inlet and outlet pressure data is preprocessed to obtain the actual DPF inlet and outlet pressure data. It can be understood that by cleaning the initial DPF inlet and outlet pressure data, some outliers can be removed, making the identification results more direct and accurate.

[0073] like Figure 6As shown, an embodiment of the second aspect of this application provides an exhaust pipe and aftertreatment leakage identification system 10, including: a first acquisition module 110 for acquiring DPF inlet and outlet pressure data of a vehicle network; a second acquisition module 120 for acquiring aftertreatment system pressure data under normal conditions; a model building module 130 for establishing an aftertreatment system pressure reference model based on the DPF inlet and outlet pressure data; a model correction module 140 for correcting the aftertreatment system pressure reference model using the aftertreatment system pressure data to obtain a DPF inlet flow resistance reference and a DPF outlet flow resistance reference; a third acquisition module 150 for acquiring the first window average value of the DPF inlet pressure flow resistance and the second window average value of the DPF outlet pressure flow resistance in real time; and an identification module 160 for obtaining an identification result based on the DPF inlet flow resistance reference, the first window average value, the DPF outlet flow resistance reference, and the second window average value.

[0074] The exhaust pipe and aftertreatment leakage identification system 10 provided in this embodiment includes a first acquisition module 110, a second acquisition module 120, a model building module 130, a model correction module 140, a third acquisition module 150, and an identification module 160. The first acquisition module 110 acquires DPF inlet and outlet pressure data from the vehicle network. The second acquisition module 120 acquires aftertreatment system pressure data under normal conditions. The model building module 130 establishes an aftertreatment system pressure reference model based on the DPF inlet and outlet pressure data. The model correction module 140 corrects the aftertreatment system pressure reference model using the aftertreatment system pressure data to obtain DPF inlet flow resistance reference and DPF outlet flow resistance reference. The third acquisition module 150 acquires the first window average value of the DPF inlet pressure flow resistance and the second window average value of the DPF outlet pressure flow resistance in real time. The identification module 160 derives the identification result based on the DPF inlet flow resistance reference, the first window average value, the DPF outlet flow resistance reference, and the second window average value. Based on DPF inlet and outlet pressure data from Engine.com, a pressure benchmark model for the aftertreatment system under normal conditions is established, which is more direct and accurate, applicable to various vehicle models, and simplifies calibration testing. Furthermore, a window averaging algorithm is used in the DPF inlet and outlet pressure readings to avoid misjudgments caused by fluctuations.

[0075] like Figure 7 As shown, an embodiment of the third aspect of this application provides an exhaust pipe and aftertreatment leak detection system 20, including: a memory 300 and a processor 400, wherein the memory 300 stores a program or instructions that can be executed on the processor 400, and when the processor 400 executes the program or instructions, it implements the steps of the exhaust pipe and aftertreatment leak detection method of any one of the embodiments of the first aspect, and thus has the technical effects of any embodiment of the first aspect, which will not be repeated here.

[0076] An embodiment of the fourth aspect of this application provides a readable storage medium having a program or instructions stored thereon. When the program or instructions are executed by a processor, they implement the steps of the exhaust pipe and aftertreatment leakage identification method of any one of the embodiments of the first aspect, and thus have the technical effects of any embodiment of the first aspect described above, which will not be repeated here.

[0077] An embodiment of the fifth aspect of this application provides an engine including an exhaust pipe and aftertreatment leak detection system 10 as described in any of the above embodiments, or an exhaust pipe and aftertreatment leak detection system 20 as described in any of the above embodiments, or a readable storage medium as described in any of the above embodiments.

[0078] The engine provided according to the embodiments of this application includes an exhaust pipe and aftertreatment leak detection system 10 as described in any of the above embodiments, or an exhaust pipe and aftertreatment leak detection system 20 as described in any of the above embodiments, or a readable storage medium as described in any of the above embodiments. Therefore, it has all the beneficial effects of the exhaust pipe and aftertreatment leak detection system 10 as described in any of the above embodiments, or an exhaust pipe and aftertreatment leak detection system 20 as described in any of the above embodiments, or a readable storage medium as described in any of the above embodiments, which will not be repeated here.

[0079] like Figure 8 As shown, according to a specific embodiment of the exhaust pipe and aftertreatment leakage identification method provided in this application, based on the big data of engine network, DPF inlet and outlet pressure big data is collected to establish a DPF inlet and outlet pressure benchmark model under normal conditions; then the real-time DPF inlet and outlet pressure is compared with the benchmark model, and if the error exceeds the set limit, the exhaust pipe system leakage is identified.

[0080] Specifically, the first step is to collect big data from the vehicle network and then clean the data. The main purpose is to remove outliers.

[0081] Secondly, it is necessary to establish a data acquisition system for the pressure status of the aftertreatment system under normal conditions. This mainly involves collecting data on engine speed, torque, exhaust flow rate, exhaust temperature, DPF inlet pressure sensor readings, DPF outlet pressure sensor readings, time since the last regeneration, DPF carbon load, total engine running time, mileage, and DPF ash content, while ensuring that these data meet the set limits.

[0082] Then, a pressure baseline model for the aftertreatment system under normal conditions is established. This baseline model consists of exhaust temperature, exhaust flow rate, DPF inlet pressure sensor readings, DPF outlet pressure sensor readings, DPF inlet flow resistance and window average value, DPF outlet flow resistance and window average value, and corrections for DPF ash content and carbon loading within 2 hours after the first successful regeneration. This baseline model serves as the benchmark for DPF inlet and outlet flow resistance under normal exhaust system conditions.

[0083] Then, the window average values ​​of the DPF inlet pressure flow resistance and the DPF outlet pressure flow resistance are collected in real time.

[0084] The average value of the DPF inlet pressure flow resistance is compared with the reference value of the DPF inlet pressure flow resistance. If this error exceeds the set limit, it is identified as a leak in the exhaust pipe before the DPF. The average value of the DPF outlet pressure flow resistance is also compared with the reference value of the DPF outlet pressure flow resistance. If this error exceeds the set limit, it is identified as a leak in the exhaust pipe from the DPF to the SCR.

[0085] In summary, the beneficial effects of the embodiments of this application are as follows:

[0086] 1. The method of collecting and cleaning DPF pressure data through vehicle-to-everything (V2X) big data is more direct and accurate.

[0087] 2. Based on DPF pressure big data, a method for establishing a DPF pressure benchmark model under normal conditions is applicable to various vehicle models and simplifies calibration test workload.

[0088] 3. Algorithm for calculating the window average value of DPF pressure flow resistance. This avoids misjudgments caused by fluctuations.

[0089] In this application, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance; the term "multiple" refers to two or more unless otherwise expressly defined. The terms "install," "connect," "link," and "fix" should be interpreted broadly. For example, "connect" can mean a fixed connection, a detachable connection, or an integral connection; "link" can mean a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0090] In the description of this application, it should be understood that the terms "upper", "lower", "front", "rear", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or module referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0091] In the description of this specification, the terms "one embodiment," "some embodiments," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0092] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for identifying air leaks in exhaust pipes and aftertreatment systems, characterized in that, include: Obtain DPF inlet and outlet pressure data; Obtain post-processing system pressure data; A pressure benchmark model for the aftertreatment system is established based on the DPF inlet and outlet pressure data. The pressure benchmark model for the aftertreatment system is a benchmark model composed of exhaust temperature, exhaust flow rate, DPF inlet pressure sensor reading, DPF outlet pressure sensor reading, DPF inlet flow resistance and window average value, DPF outlet flow resistance and window average value, DPF ash content and carbon loading correction within 2 hours after the first successful regeneration. The benchmark model is the benchmark for DPF inlet flow resistance and DPF outlet flow resistance under normal exhaust system conditions. The pressure reference model of the post-processing system is corrected by using the characteristic parameters of the post-processing system to obtain the DPF inlet flow resistance reference and the DPF outlet flow resistance reference. Real-time acquisition of the first window average value of DPF inlet pressure flow resistance and the second window average value of DPF outlet pressure flow resistance; The identification result is obtained based on the DPF inlet flow resistance reference, the average value of the first window, the DPF outlet flow resistance reference, and the average value of the second window. The aftertreatment system pressure data includes one or a combination of the following: engine speed, torque, exhaust flow rate, exhaust temperature, DPF inlet pressure sensor reading, DPF outlet pressure sensor reading, time since last regeneration, DPF carbon load, total engine running time, mileage, and DPF ash content. The step of correcting the pressure reference model of the post-processing system using characteristic parameters of the post-processing system to obtain the DPF inlet flow resistance reference and the DPF outlet flow resistance reference specifically includes: Based on the pressure data of the post-treatment system, characteristic parameters are obtained within 2 hours after the first successful regeneration. These characteristic parameters include: exhaust temperature, exhaust flow rate, DPF inlet pressure sensor reading, DPF outlet pressure sensor reading, DPF inlet flow resistance, average inlet flow resistance window value, DPF outlet flow resistance, average outlet flow resistance window value, DPF ash content, and DPF carbon loading. The pressure reference model of the aftertreatment system is corrected by the characteristic parameters to obtain the DPF inlet flow resistance reference and the DPF outlet flow resistance reference.

2. The method for identifying exhaust pipe and aftertreatment leakage according to claim 1, characterized in that, The process of obtaining the identification result based on the DPF inlet flow resistance reference, the average value of the first window, the DPF outlet flow resistance reference, and the average value of the second window specifically includes: The inlet pressure error is derived based on the DPF inlet flow resistance reference and the average value of the first window. Determine whether the inlet pressure error is greater than a first set value; If so, the identification result is that there is a leak in the exhaust pipe at the front of the DPF.

3. The method for identifying exhaust pipe and aftertreatment leakage according to claim 2, characterized in that, The step of obtaining the identification result based on the DPF inlet flow resistance reference, the average value of the first window, the DPF outlet flow resistance reference, and the average value of the second window also includes: The outlet pressure error is derived based on the DPF outlet flow resistance reference and the average value of the second window. Determine whether the outlet pressure error is greater than the second set value; If so, the identification result is that there is a leak in the exhaust pipe from the DPF to the SCR section.

4. The method for identifying exhaust pipe and aftertreatment leaks according to any one of claims 1 to 3, characterized in that, Obtaining import and export pressure data specifically includes: Obtain initial DPF inlet and outlet pressure data for the vehicle-to-everything (V2X) network; The initial DPF inlet and outlet pressure data are preprocessed to obtain the DPF inlet and outlet pressure data.

5. A system for detecting air leaks in exhaust pipes and aftertreatment systems, characterized in that, include: The first acquisition module (110) is used to acquire the DPF inlet and outlet pressure data of the vehicle network; The second acquisition module (120) is used to acquire the aftertreatment system pressure data under normal conditions; the aftertreatment system pressure reference model is a reference model composed of exhaust temperature, exhaust flow rate, DPF inlet pressure sensor reading, DPF outlet pressure sensor reading, DPF inlet flow resistance and window average value, DPF outlet flow resistance and window average value, DPF ash content and carbon loading correction within 2 hours after the first successful regeneration. The reference model is the DPF inlet flow resistance and DPF outlet flow resistance reference under normal exhaust system conditions. The model building module (130) is used to build a pressure reference model for the after-treatment system based on the DPF inlet and outlet pressure data. The model correction module (140) is used to correct the pressure reference model of the post-processing system through the characteristic parameters of the post-processing system to obtain the DPF inlet flow resistance reference and the DPF outlet flow resistance reference. The third acquisition module (150) is used to acquire the first window average value of the DPF inlet pressure flow resistance and the second window average value of the DPF outlet pressure flow resistance in real time. The identification module (160) is used to obtain the identification result based on the DPF inlet flow resistance reference, the average value of the first window, the DPF outlet flow resistance reference, and the average value of the second window; The pressure data of the aftertreatment system includes one or a combination of the following: engine speed, torque, exhaust flow rate, exhaust temperature, DPF inlet pressure sensor reading, DPF outlet pressure sensor reading, time since last regeneration, DPF carbon load, total engine running time, mileage, and DPF ash content. The step of correcting the pressure reference model of the post-processing system using characteristic parameters of the post-processing system to obtain the DPF inlet flow resistance reference and the DPF outlet flow resistance reference specifically includes: Based on the pressure data of the post-treatment system, characteristic parameters are obtained within 2 hours after the first successful regeneration. These characteristic parameters include: exhaust temperature, exhaust flow rate, DPF inlet pressure sensor reading, DPF outlet pressure sensor reading, DPF inlet flow resistance, average inlet flow resistance window value, DPF outlet flow resistance, average outlet flow resistance window value, DPF ash content, and DPF carbon loading. The pressure reference model of the aftertreatment system is corrected by the characteristic parameters to obtain the DPF inlet flow resistance reference and the DPF outlet flow resistance reference.

6. A system for detecting leaks in exhaust pipes and aftertreatment systems, characterized in that, include: A memory (300) and a processor (400), wherein the memory (300) stores a program or instructions executable on the processor (400), and the processor (400) implements the steps of the exhaust pipe and aftertreatment leak detection method as described in any one of claims 1 to 4 when executing the program or instructions.

7. A readable storage medium having a program or instructions stored thereon, characterized in that, When the program or the instructions are executed by the processor, they implement the steps of the exhaust pipe and aftertreatment leak detection method as described in any one of claims 1 to 4.

8. An engine, characterized in that, include: The exhaust pipe and aftertreatment leak detection system as described in claim 5; and / or The exhaust pipe and aftertreatment leak detection system as described in claim 6; and / or The readable storage medium as described in claim 7.

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