Blockage detection method and system for particle catcher, vehicle and equipment

Abstract

The invention discloses a blockage detection method and system for a particle catcher, a vehicle and equipment. The blockage detection method for the particle catcher comprises the steps that the upstream pressure, the downstream pressure, the exhaust flow and engine operation parameters of the particle catcher are obtained; the parameters are input into a preset carbon carrying capacity model, the carbon carrying capacity of the particle trapper is calculated, and the carbon carrying capacity model is pre-established according to the mapping relation between historical data and comprises different dust deposition blocking degrees of the particle trapper and the corresponding particle trapper parameters under the different dust deposition blocking degrees; and according to the carbon loading amount of the particle catcher, determining the ash deposition blocking degree of the particle catcher. By adopting the method and the device, the carbon load of the particle catcher can be accurately calculated on the basis of the relation model fusing the upstream pressure, the downstream pressure, the exhaust flow and the engine operation parameters, and then the dust deposition blockage degree of the particle catcher is determined, so that the blockage detection reliability is improved, and the fuel consumption or the blockage risk is reduced.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, system, vehicle, and device for detecting blockages in a particulate filter. Background Technology

[0002] Particulate filters are commonly used components in modern new energy vehicles. After prolonged use, their pipes are prone to clogging with dust or oil, leading to distortion of the measured differential pressure signal. Traditional control methods rely solely on absolute differential pressure thresholds for identification, making it difficult to distinguish between actual blockages and sensor clogging. Existing solutions typically use differential pressure sensors to detect the absolute pressure at the particulate filter's inlet and outlet. Based on the pressure difference and exhaust flow rate, they calculate the cumulative amount of carbon soot and dust inside the particulate filter to determine blockages and other faults. However, in actual use, uneven dust distribution can cause the pressure at the differential pressure sensor sampling point to inaccurately reflect the carbon load of the particulate filter, resulting in distorted cumulative amount calculations. This can lead to misjudgments of the particulate filter's regeneration needs, increasing fuel consumption or increasing the risk of blockages. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, system, vehicle, and equipment for detecting blockage in particulate filters to address the aforementioned technical problems. This method can accurately calculate the carbon load of the particulate filter based on a relationship model that integrates upstream pressure, downstream pressure, exhaust flow, and engine operating parameters, thereby determining the degree of ash accumulation and blockage. This improves the reliability of blockage detection and reduces fuel consumption or the risk of blockage.

[0004] Firstly, a method for detecting clogging in a particulate filter is provided, comprising: Obtain the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter; The upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters are input into a preset carbon load model to calculate the carbon load of the particulate filter. The carbon load model is pre-built based on the mapping relationship between historical data, which includes different degrees of ash accumulation and blockage of the particulate filter, as well as the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter under different degrees of ash accumulation and blockage. The degree of ash accumulation and blockage in the particulate filter is determined based on its carbon load.

[0005] Furthermore, before inputting the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters into a preset carbon load model to calculate the carbon load of the particulate filter using the carbon load model, the method further includes: Obtain the historical data; Through bench tests and actual road tests, the corresponding relationships between the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter under the degree of dust accumulation and blockage were obtained; The carbon loading model is constructed based on the aforementioned correspondence.

[0006] Further, determining the degree of ash accumulation and clogging of the particulate filter based on its carbon loading includes: Compare the carbon loading of the particle trap with a preset threshold; The degree of dust accumulation and blockage in the particle trap is determined based on the comparison results.

[0007] Further, determining the degree of ash accumulation and clogging of the particulate filter based on the comparison results includes: If the carbon load of the particulate trap is greater than the preset threshold, then the degree of ash accumulation and blockage is determined to meet the lower limit index of the degree of ash accumulation and blockage. If the carbon load of the particulate trap is less than or equal to the preset threshold, then the degree of ash accumulation and blockage is determined to be within the normal range.

[0008] Furthermore, the preset threshold is determined in advance based on the performance parameters of the particulate filter and the engine emission standards.

[0009] Furthermore, it also includes: The pressure signal output by the differential pressure sensor of the particle trap is sampled to obtain a pressure value sequence; Calculate the mean and standard deviation of the pressure value sequence; Based on the mean and standard deviation of the pressure value sequence, determine whether there is any abnormality in the pressure signal output by the differential pressure sensor.

[0010] Further, determining whether there is an anomaly in the pressure signal output by the differential pressure sensor based on the mean and standard deviation of the pressure value sequence includes: Determine whether the mean of the pressure value sequence is within a preset mean range; Determine whether the standard deviation of the pressure value sequence is within a preset standard deviation range; If the mean of the pressure value sequence is within a preset mean range and the standard deviation of the pressure value sequence is within a preset standard deviation range, then the pressure signal output by the differential pressure sensor is determined to be normal; otherwise, the pressure signal output by the differential pressure sensor is determined to be abnormal. The preset mean range and the preset standard deviation range are obtained by pre-calibration.

[0011] Secondly, a clogging detection system for a particulate filter is provided, comprising: The acquisition module is used to obtain the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter; The carbon load calculation module is used to input the upstream pressure, downstream pressure, exhaust flow rate and engine operating parameters into a preset carbon load model, so as to calculate the carbon load of the particulate filter through the carbon load model. The carbon load model is pre-built based on the mapping relationship between historical data. The historical data includes different degrees of ash accumulation and blockage of the particulate filter, and the upstream pressure, downstream pressure, exhaust flow rate and engine operating parameters of the particulate filter under different degrees of ash accumulation and blockage. The detection module is used to determine the degree of ash accumulation and blockage of the particulate filter based on the carbon load of the particulate filter.

[0012] Thirdly, a vehicle is provided, comprising: a blockage detection system for the particulate filter according to the second aspect described above.

[0013] Fourthly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the steps of the blockage detection method for the particle trap described in the first aspect and any possible implementation of the first aspect.

[0014] In the embodiments of this application, the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter are first obtained. Then, these parameters are input into a pre-set carbon load model to calculate the carbon load of the particulate filter. The carbon load model is pre-built based on the mapping relationship between historical data, including different degrees of ash accumulation and clogging of the particulate filter, and the corresponding upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters for each degree of ash accumulation and clogging. Finally, the degree of ash accumulation and clogging of the particulate filter is determined based on its carbon load. Therefore, the carbon load of the particulate filter can be accurately calculated based on a relationship model integrating upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters, thereby determining its degree of ash accumulation and clogging, improving the reliability of clogging detection, and reducing fuel consumption or clogging risk. Attached Figure Description

[0015] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 A flowchart of a blockage detection method for a particulate filter provided in an embodiment of this application; Figure 2 A schematic diagram illustrating the execution of the blockage detection method for the particulate trap provided in this application embodiment; Figure 3 This is a structural block diagram of the blockage detection system for the particulate trap provided in the embodiments of this application; Figure 4 This is a structural block diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0016] The present application will now be described in further detail with reference to the embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the application. Furthermore, it should be noted that, for ease of description, only the parts relevant to the application are shown in the accompanying drawings.

[0017] It should be noted that, unless otherwise specified, the embodiments and features of the embodiments in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] The following describes in detail, with reference to the accompanying drawings, a blockage detection method, system, vehicle, and device for a particulate trap according to embodiments of this application.

[0019] Figure 1 This is a flowchart of a blockage detection method for a particulate filter according to an embodiment of this application. Figure 1 As shown, and in combination Figure 2 The clogging detection method for a particulate filter according to an embodiment of this application includes the following steps: S101: Obtain the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter.

[0020] S102: Input the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters into a preset carbon load model to calculate the carbon load of the particulate filter. The carbon load model is pre-built based on the mapping relationship between historical data. The historical data includes different degrees of ash accumulation and blockage of the particulate filter, as well as the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter corresponding to different degrees of ash accumulation and blockage.

[0021] In one embodiment of this application, before inputting the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters into a preset carbon load model to calculate the carbon load of the particulate filter through the carbon load model, the method further includes: obtaining the historical data; obtaining the correspondence between the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter under the degree of ash accumulation and blockage through bench tests and actual road tests; and constructing the carbon load model based on the correspondence.

[0022] Specifically, the carbon loading model is built based on machine learning or regression analysis, and its general expression is shown in Formula 1: (1) in, y Indicates the carbon loading of the particulate filter; f This represents the mapping function obtained through training with bench test and road test data; P1 is the upstream pressure; P2 is the downstream pressure; Q is the exhaust flow rate; and n is the engine speed.

[0023] If determined through data fitting y and P1 , P2 , Q , n It satisfies a linear relationship, and its specific expression can be written as Formula 2: (2) Where: a0 is the model intercept term (dimensionless, obtained from data fitting); a1, a2, a3, and a4 are the regression coefficients of P1, P2, Q, and n, respectively.

[0024] S103: Determine the degree of ash accumulation and blockage of the particulate filter based on the carbon load of the particulate filter.

[0025] In one embodiment of this application, determining the degree of ash accumulation and blockage of the particulate filter based on its carbon loading includes: comparing the carbon loading of the particulate filter with a preset threshold; and determining the degree of ash accumulation and blockage of the particulate filter based on the comparison result.

[0026] In one embodiment of this application, determining the degree of ash accumulation and blockage of the particulate filter based on the comparison result includes: if the carbon load of the particulate filter is greater than the preset threshold, then determining that the degree of ash accumulation and blockage meets the lower limit index of the degree of ash accumulation and blockage; if the carbon load of the particulate filter is less than or equal to the preset threshold, then determining that the degree of ash accumulation and blockage is within the normal range.

[0027] If the degree of ash accumulation and blockage meets the lower limit index, it indicates that the particle trap has a problem with ash accumulation and blockage.

[0028] In one embodiment of this application, the preset threshold is predetermined based on the performance parameters of the particulate filter and engine emission standards.

[0029] In one specific example, the threshold is set to 0.7, but in other examples, it can be set according to actual needs.

[0030] In one embodiment of this application, the method further includes: sampling the pressure signal output by the differential pressure sensor of the particle trap to obtain a pressure value sequence; calculating the mean and standard deviation of the pressure value sequence; and determining whether there is an abnormality in the pressure signal output by the differential pressure sensor based on the mean and standard deviation of the pressure value sequence.

[0031] Specifically, the pressure value sequence is represented as follows: The mean and standard deviation of the pressure value series are shown in Formulas 3 and 4: (3) (4) in, t i For the first i Each sampling time; P(t i ) For the first i Each sampling time The corresponding pressure value; N This represents the number of sampling points; This is the mean of the pressure value sequence; is the standard deviation of the pressure value series.

[0032] In one embodiment of this application, determining whether the pressure signal output by the differential pressure sensor is abnormal based on the mean and standard deviation of the pressure value sequence includes: determining whether the mean of the pressure value sequence is within a preset mean range; determining whether the standard deviation of the pressure value sequence is within a preset standard deviation range; if the mean of the pressure value sequence is within the preset mean range and the standard deviation of the pressure value sequence is within the preset standard deviation range, then the pressure signal output by the differential pressure sensor is determined to be normal; otherwise, the pressure signal output by the differential pressure sensor is determined to be abnormal, wherein the preset mean range and the preset standard deviation range are pre-calibrated.

[0033] According to the particulate filter clogging detection method of this application embodiment, the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter are first obtained. Then, these parameters are input into a pre-set carbon load model to calculate the carbon load of the particulate filter. The carbon load model is pre-built based on the mapping relationship between historical data, including different degrees of ash accumulation and clogging of the particulate filter, and the corresponding upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters for each degree of ash accumulation and clogging. Finally, the degree of ash accumulation and clogging of the particulate filter is determined based on its carbon load. Therefore, the carbon load of the particulate filter can be accurately calculated based on a relationship model integrating upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters, thereby determining its degree of ash accumulation and clogging, improving the reliability of clogging detection, and reducing fuel consumption or clogging risk.

[0034] Figure 3 This is a structural block diagram of a blockage detection system for a particulate filter according to an embodiment of this application. Figure 3 As shown, the clogging detection system for a particulate filter according to an embodiment of this application includes: an acquisition module 310, a carbon loading calculation module 320, and a detection module 330, wherein: The acquisition module 310 is used to obtain the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter; The carbon load calculation module 320 is used to input the upstream pressure, downstream pressure, exhaust flow rate and engine operating parameters into a preset carbon load model, so as to calculate the carbon load of the particulate filter through the carbon load model. The carbon load model is pre-built based on the mapping relationship between historical data. The historical data includes different degrees of ash accumulation and blockage of the particulate filter, and the upstream pressure, downstream pressure, exhaust flow rate and engine operating parameters of the particulate filter under different degrees of ash accumulation and blockage. The detection module 330 is used to determine the degree of ash accumulation and blockage of the particulate filter based on the carbon load of the particulate filter.

[0035] According to the particulate filter clogging detection system of this application embodiment, the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter are first obtained. Then, these parameters are input into a pre-set carbon load model to calculate the carbon load of the particulate filter. The carbon load model is pre-built based on the mapping relationship between historical data, including different degrees of ash accumulation and clogging of the particulate filter, and the corresponding upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters for each degree of ash accumulation and clogging. Finally, the degree of ash accumulation and clogging of the particulate filter is determined based on its carbon load. Therefore, the carbon load of the particulate filter can be accurately calculated based on a relationship model integrating upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters, thereby determining its degree of ash accumulation and clogging, improving the reliability of clogging detection, and reducing fuel consumption or clogging risk.

[0036] Specific limitations regarding the blockage detection system for the particulate filter can be found in the limitations of the blockage detection method for the particulate filter described above, and will not be repeated here. Each module of the blockage detection system for the particulate filter described above can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0037] In one embodiment, a vehicle is provided, comprising: a particulate filter clogging detection system according to any of the above embodiments. The vehicle first obtains the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter; then, it inputs the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters into a pre-set carbon load model to calculate the carbon load of the particulate filter. The carbon load model is pre-built based on a mapping relationship between historical data, including different degrees of ash accumulation and clogging of the particulate filter, and the corresponding upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters for each degree of ash accumulation and clogging. Finally, the degree of ash accumulation and clogging of the particulate filter is determined based on its carbon load. Therefore, the carbon load of the particulate filter can be accurately calculated based on a relationship model integrating upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters, thereby determining its degree of ash accumulation and clogging, improving the reliability of clogging detection, and reducing fuel consumption or clogging risk.

[0038] Furthermore, other components and functions of the vehicle according to the embodiments of this application are known to those skilled in the art and will not be described in detail here.

[0039] In one embodiment, a computer device is provided. Figure 4 This is a structural block diagram of the computer device provided in the embodiments of this application, with reference to... Figure 4 The computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned embodiment of the blockage detection method for the particulate filter. For example, it executes the following: obtaining the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter; The upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters are input into a preset carbon load model to calculate the carbon load of the particulate filter. The carbon load model is pre-built based on the mapping relationship between historical data, which includes different degrees of ash accumulation and blockage of the particulate filter, as well as the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter under different degrees of ash accumulation and blockage. The degree of ash accumulation and blockage in the particulate filter is determined based on its carbon load.

[0040] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0041] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0042] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for detecting clogging in a particulate filter, characterized in that, include: Obtain the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter; The upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters are input into a preset carbon load model to calculate the carbon load of the particulate filter. The carbon load model is pre-built based on the mapping relationship between historical data, which includes different degrees of ash accumulation and blockage of the particulate filter, as well as the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter under different degrees of ash accumulation and blockage. The degree of ash accumulation and blockage in the particulate filter is determined based on its carbon load.

2. The clogging detection method for a particulate filter according to claim 1, characterized in that, Before inputting the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters into a preset carbon load model to calculate the carbon load of the particulate filter using the carbon load model, the process further includes: Obtain the historical data; Through bench tests and actual road tests, the corresponding relationships between the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter under the degree of dust accumulation and blockage were obtained; The carbon loading model is constructed based on the aforementioned correspondence.

3. The clogging detection method for a particulate trap according to claim 1, characterized in that, Determining the degree of ash accumulation and clogging of the particulate filter based on its carbon load includes: Compare the carbon loading of the particle trap with a preset threshold; The degree of dust accumulation and blockage in the particle trap is determined based on the comparison results.

4. The clogging detection method for a particulate trap according to claim 3, characterized in that, Determining the degree of ash accumulation and clogging of the particulate filter based on the comparison results includes: If the carbon load of the particulate trap is greater than the preset threshold, then the degree of ash accumulation and blockage is determined to meet the lower limit index of the degree of ash accumulation and blockage. If the carbon load of the particulate trap is less than or equal to the preset threshold, then the degree of ash accumulation and blockage is determined to be within the normal range.

5. The clogging detection method for a particulate trap according to claim 4, characterized in that, The preset threshold is determined in advance based on the performance parameters of the particulate filter and the engine emission standards.

6. The method for detecting blockage in a particulate trap according to any one of claims 1-5, characterized in that, Also includes: The pressure signal output by the differential pressure sensor of the particle trap is sampled to obtain a pressure value sequence. Calculate the mean and standard deviation of the pressure value sequence; Based on the mean and standard deviation of the pressure value sequence, determine whether there is any abnormality in the pressure signal output by the differential pressure sensor.

7. The clogging detection method for a particulate trap according to claim 6, characterized in that, The step of determining whether there is an anomaly in the pressure signal output by the differential pressure sensor based on the mean and standard deviation of the pressure value sequence includes: Determine whether the mean of the pressure value sequence is within a preset mean range; Determine whether the standard deviation of the pressure value sequence is within a preset standard deviation range; If the mean of the pressure value sequence is within a preset mean range and the standard deviation of the pressure value sequence is within a preset standard deviation range, then the pressure signal output by the differential pressure sensor is determined to be normal; otherwise, the pressure signal output by the differential pressure sensor is determined to be abnormal. The preset mean range and the preset standard deviation range are obtained by pre-calibration.

8. A clogging detection system for a particulate filter, characterized in that, include: The acquisition module is used to obtain the upstream pressure, downstream pressure, exhaust flow rate, and engine operating parameters of the particulate filter; The carbon load calculation module is used to input the upstream pressure, downstream pressure, exhaust flow rate and engine operating parameters into a preset carbon load model, so as to calculate the carbon load of the particulate filter through the carbon load model. The carbon load model is pre-built based on the mapping relationship between historical data. The historical data includes different degrees of ash accumulation and blockage of the particulate filter, and the upstream pressure, downstream pressure, exhaust flow rate and engine operating parameters of the particulate filter under different degrees of ash accumulation and blockage. The detection module is used to determine the degree of ash accumulation and blockage of the particulate filter based on the carbon load of the particulate filter.

9. A vehicle, characterized in that, include: The clogging detection system for the particulate trap according to claim 8.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the blockage detection method for the particulate trap according to any one of claims 1-7.

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