A correction method, device, medium and equipment for differential pressure sensor

Abstract

The present application discloses a correction method, device, medium and equipment for a differential pressure sensor, which relates to the field of vehicle technology. The differential pressure sensor is used to detect the pressure difference between the upstream and downstream of the diesel particulate filter DPF of the post-treatment system. The method includes: obtaining the zero drift value of the differential pressure sensor; if the zero drift value of the differential pressure sensor is greater than a preset threshold, using the Kalman differential pressure model obtained by pre-learning to obtain the theoretical value of the differential pressure sensor; using the theoretical value to correct the differential pressure sensor to obtain the corrected value of the differential pressure sensor. It can be seen that in this method, the theoretical value of the Kalman differential pressure model is used to correct the value of the differential pressure sensor, thereby improving the accuracy of the differential pressure sensor and reducing the error of the differential pressure sensor.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a correction method, device, medium and equipment for a differential pressure sensor. Background Art

[0002] With the development of vehicle technology, in order to reduce the emission of toxic and harmful gases in exhaust gas, vehicles will be equipped with after-treatment systems. The diesel particulate filter (DPF) in the after-treatment box of the after-treatment system reduces the particulate matter in the exhaust gas. In order to determine whether there is a fault in the after-treatment system, a differential pressure sensor is usually set in the after-treatment box to measure the pressure difference between the upstream and downstream of the after-treatment box, and subsequent fault diagnosis is performed based on the pressure difference.

[0003] However, in actual applications, there are situations such as insufficient consistency of the differential pressure sensor and changes in the resistance of the wiring harness, which will cause deviations in the measurement of the differential pressure sensor. Based on the deviated differential pressure, subsequent misdiagnosis will occur. Summary of the invention

[0004] The present application provides a correction method, device, medium and equipment for a differential pressure sensor, which can improve the accuracy of the detection results of the differential pressure sensor.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a correction method for a differential pressure sensor, wherein the differential pressure sensor is used to detect the pressure difference between the upstream and downstream of a diesel particulate filter DPF of a post-treatment system, the method comprising:

[0007] Obtaining a zero drift value of the differential pressure sensor;

[0008] If the zero drift value of the differential pressure sensor is greater than a preset threshold, the theoretical value of the differential pressure sensor is obtained by using the pre-learned Kalman differential pressure model;

[0009] The differential pressure sensor is corrected using the theoretical value to obtain a corrected value of the differential pressure sensor.

[0010] In some possible implementations, the method further includes:

[0011] If the zero drift value of the differential pressure sensor is less than or equal to the preset threshold, the parameters of the Kalman differential pressure model are updated using the zero drift value.

[0012] In some possible implementations, the method further includes:

[0013] Fault diagnosis is performed based on the correction value of the differential pressure sensor.

[0014] In some possible implementations, the equation of the Kalman pressure difference model is:

[0015] Δp=AρV 2 +BμV+Cg(x)

[0016] Wherein, △p is the theoretical value of the differential pressure sensor, ρ is the exhaust density of the vehicle, V is the exhaust volume flow of the vehicle, μ is the exhaust dynamic viscosity coefficient of the vehicle, g(x) is the zero drift value of the differential pressure sensor, and A, B and C are coefficients.

[0017] In some possible implementations, the A, the B, and the C are determined in the following manner:

[0018] θ=[A,B,C] T

[0019] φ=[ρV 2 ,μV,g(x)] T

[0020] Δp mk =φ T θ k +v

[0021] θ k =θ k-1 +w

[0022] In which, let the prior estimate be The posterior estimate is v and w are external disturbances, and K is a parameter of the Kalman pressure difference model.

[0023] In a second aspect, the present application provides a correction device for a differential pressure sensor, wherein the differential pressure sensor is used to detect the pressure difference between the upstream and downstream of a diesel particulate filter DPF of a post-treatment system, and the device comprises:

[0024] An acquisition unit, used for acquiring a zero drift value of the differential pressure sensor;

[0025] A comparison unit, configured to obtain a theoretical value of the differential pressure sensor by using a pre-learned Kalman differential pressure model if the zero drift value of the differential pressure sensor is greater than a preset threshold;

[0026] The correction unit is used to correct the pressure difference sensor using the theoretical value to obtain a correction value of the pressure difference sensor.

[0027] In some possible implementations, the comparison unit is further configured to update the parameters of the Kalman pressure difference model using the zero drift value if the zero drift value of the pressure difference sensor is less than or equal to the preset threshold.

[0028] In some possible implementations, the device further includes: a diagnosis unit;

[0029] The diagnosis unit is used to perform fault diagnosis based on the correction value of the differential pressure sensor.

[0030] In some possible implementations, the equation of the Kalman pressure difference model is:

[0031] Δp=AρV 2 +BμV+Cg(x)

[0032] Wherein, △p is the theoretical value of the differential pressure sensor, ρ is the exhaust density of the vehicle, V is the exhaust volume flow of the vehicle, μ is the exhaust dynamic viscosity coefficient of the vehicle, g(x) is the zero drift value of the differential pressure sensor, and A, B and C are coefficients.

[0033] In some possible implementations, the A, the B, and the C are determined in the following manner:

[0034] θ=[A,B,C] T

[0035] φ=[ρV 2 ,μV,g(x)] T

[0036] Δp mk =φ T θ k +v

[0037] θ k =θ k-1 +w

[0038] In which, let the prior estimate be The posterior estimate is v and w are external disturbances, and K is a parameter of the Kalman pressure difference model.

[0039] In a third aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and the computer program is used to execute any one of the methods described in the first aspect.

[0040] In a fourth aspect, the present application provides a device comprising: a memory and a processor; a computer program is stored in the memory; and the processor is used to execute the computer program in the memory to implement any one of the methods described in the first aspect.

[0041] It can be seen from the above technical solution that the technical solution of this application has the following beneficial effects:

[0042] The present application provides a correction method for a differential pressure sensor, the method comprising obtaining a zero drift value of the differential pressure sensor, and if the zero drift value of the differential pressure sensor is greater than a preset threshold, using a pre-learned Kalman differential pressure model to obtain a theoretical value of the differential pressure sensor; and using the theoretical value to correct the differential pressure sensor to obtain a corrected value of the differential pressure sensor. It can be seen that in this method, the theoretical value of the Kalman differential pressure model is used to correct the value of the differential pressure sensor, thereby improving the accuracy of the differential pressure sensor and reducing the error of the differential pressure sensor.

[0043] It should be understood that the description of technical features, technical solutions, beneficial effects or similar language in this application does not imply that all features and advantages can be realized in any single embodiment. On the contrary, it is understood that the description of features or beneficial effects means that specific technical features, technical solutions or beneficial effects are included in at least one embodiment. Therefore, the description of technical features, technical solutions or beneficial effects in this specification does not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions and beneficial effects described in the present embodiment can also be combined in any appropriate manner. Those skilled in the art will understand that the embodiment can be realized without one or more specific technical features, technical solutions or beneficial effects of a specific embodiment. In other embodiments, additional technical features and beneficial effects can also be identified in a specific embodiment that does not embody all embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A flowchart of a correction method for a differential pressure sensor provided in an embodiment of the present application;

[0045] Figure 2 A schematic diagram of a correction device for a differential pressure sensor provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The terms "first", "second", "third", etc. in the specification, claims and drawings of this application are used to distinguish different objects rather than to limit a specific order.

[0047] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0048] In order to make the description of the following embodiments clear and concise, a brief introduction to the related technology is first given.

[0049] The diesel particulate filter (DPF) is used to capture particulate matter in the exhaust gas, such as soot, etc. A differential pressure sensor is usually configured in the after-treatment system to measure the pressure difference between the upstream and downstream of the DPF.

[0050] Due to the capture mechanism of DPF, particulate matter will gradually accumulate inside the DPF. In order to monitor the accumulation of particulate matter inside the DPF, the current mainstream solution is to monitor it through a differential pressure sensor. The basic principle is that when the particulate matter inside the DPF accumulates more, the corresponding channel gap becomes smaller, making the pressure difference between the two ends of the DPF greater. However, in actual application, there are situations such as insufficient consistency of the differential pressure sensor and changes in the resistance of the wiring harness, which lead to deviations in sensor measurement and cause DPF regeneration or misdiagnosis.

[0051] Currently, after the vehicle is powered off, the current pressure difference value of the pressure difference sensor can be obtained as the drift value, and then the drift values ​​of the most recent multiple driving cycles are weighted averaged to obtain the correction value of the pressure difference sensor. Then, in the next driving cycle, the correction value and the actual measured value of the pressure difference sensor are summed to obtain the final value of the pressure difference sensor.

[0052] Another way is to further consider the effect of temperature on the pressure difference on the basis of the above solution, and further avoid the effect of temperature on the drift of the pressure difference sensor.

[0053] In the above two schemes, the zero drift value of the differential pressure sensor is detected after the whole vehicle is powered off, and the zero drift value is used as the correction value of the differential pressure sensor. Although the differential pressure sensor can be corrected to a certain extent, the drift of the differential pressure sensor is random during its actual operation, and after the whole vehicle is powered on again, it is not determined whether the differential pressure sensor is normal. The correction based only on the zero drift value at power off may be inaccurate.

[0054] In view of this, an embodiment of the present application provides a correction method for a differential pressure sensor, which can be executed by a correction device, wherein the correction device can be an ECU or other control unit on a vehicle.

[0055] Specifically, the differential pressure sensor is used to detect the differential pressure between the upstream and downstream of the diesel particulate filter DPF of the post-treatment system. The method includes: obtaining the zero drift value of the differential pressure sensor; if the zero drift value of the differential pressure sensor is greater than a preset threshold, using the Kalman differential pressure model learned in advance to obtain the theoretical value of the differential pressure sensor; using the theoretical value to correct the differential pressure sensor to obtain the corrected value of the differential pressure sensor. It can be seen that in this method, the theoretical value of the Kalman differential pressure model is used to correct the value of the differential pressure sensor, thereby improving the accuracy of the differential pressure sensor and reducing the error of the differential pressure sensor.

[0056] In order to make the technical solution of the present application clearer and easier to understand, the technical solution of the present application is introduced below in conjunction with the accompanying drawings.

[0057] like Figure 1 As shown in FIG. 1 , the figure is a flow chart of a method for identifying icing risk of a differential pressure sensor provided in an embodiment of the present application, the method comprising:

[0058] S101. A correction device obtains a zero drift value of the differential pressure sensor.

[0059] The zero drift value refers to the error of the differential pressure sensor. In some embodiments, after the vehicle is powered off, the pressure difference between the upstream and downstream of the DPF should be close to 0, but the value detected by the differential pressure sensor is not close to 0. At this time, the value detected by the differential pressure sensor is recorded as the zero drift value, which can also be called the error of the differential pressure sensor.

[0060] In some embodiments, the correction device may obtain the zero drift value of the differential pressure sensor for a plurality of driving cycles, and then perform weighted average processing to obtain the final zero drift value of the differential pressure sensor.

[0061] S102: If the zero drift value of the differential pressure sensor is greater than a preset threshold, a correction device uses a pre-learned Kalman differential pressure model to obtain a theoretical value of the differential pressure sensor.

[0062] The preset threshold may be a preset value, and the preset threshold may be a value close to 0, thereby reducing errors.

[0063] If the zero drift value of the differential pressure sensor is greater than a preset threshold, it indicates that the value detected by the differential pressure sensor is not a value close to 0, that is, there is an error in the differential pressure sensor.

[0064] At this time, the correction device can use the pre-learned Kalman pressure difference model to obtain the theoretical value of the pressure difference sensor.

[0065] Among them, the equation of the Kalman pressure difference model is:

[0066] Δp=AρV 2 +BμV+Cg(x);

[0067] Among them, △p is the theoretical value of the differential pressure sensor, ρ is the exhaust density of the vehicle, V is the exhaust volume flow rate of the vehicle, μ is the exhaust dynamic viscosity coefficient of the vehicle, g(x) is the zero drift value of the differential pressure sensor, and in some embodiments, g(x) can be obtained by calibration, and A, B and C are coefficients.

[0068] Where, Δp=AρV 2 +BμV+Cg(x) can be simplified by the following formula:

[0069] Δp=Δp in / out +Δp wall +Δp channel +Δp soot

[0070] =K in / out (ρv 2 / 2)+(μ / K w )vw+βρv 2 +4f(L / d)(ρv 2 / 2)+(μ / K soot )vw soot

[0071] =[K in / out / 2+β+4f(L / d)](ρv 2 / 2)+(μ / K w )vw+(μ / K soot )vw soot ;

[0072] Among them, K in / out is the friction coefficient at the inlet and outlet, which is related to the cross-sectional area; β is the inertial resistance coefficient; f is the friction coefficient of the gas in the pore; L is the pore length; d is the pore width; △pin / out is the pressure value of the inlet and outlet, △pwall is the pressure value of the wall, △pchannel is the pressure value of the pore, and △psoot is the pressure value of the carbon layer.

[0073] Wherein, A, B and C are determined by the following method:

[0074] θ=[A,B,C] T ;

[0075] φ=[ρV 2 ,μV,g(x)] T ;

[0076] Then, the Kalman filter observation equation and state equation are as follows:

[0077] Δp mk =φ T θ k +v

[0078] θ k =θ k-1 +w;

[0079] Among them, v and w are external interferences, and their variances are Q and R respectively.

[0080] Let the prior estimate be The posterior estimate is

[0081] In order to make the estimated value close to the actual value, it is necessary to iterate and find the parameter K so that the variance between the actual value and the estimated value is the smallest. The variance is set to e k , the covariance matrix is ​​M k ,but:

[0082]

[0083]

[0084] The evaluation indicators are as follows:

[0085]

[0086] Taking the derivative of K and setting the derivative to 0, we get:

[0087]

[0088] That is, the Kalman filter parameter K is:

[0089]

[0090] The prior values ​​of the prior errors and their covariance matrix are:

[0091]

[0092]

[0093] The covariance matrix of the updated error is:

[0094]

[0095] Substituting K into the equation, we get

[0096]

[0097] The theoretical value of the pressure difference of the Kalman filter model can be calculated by the above formula.

[0098] S103: The correction device corrects the pressure difference sensor using the theoretical value to obtain a correction value of the pressure difference sensor.

[0099] After obtaining the theoretical value of the differential pressure sensor, the correction device uses the theoretical value to correct the differential pressure sensor, thereby obtaining the accurate value of the differential pressure sensor.

[0100] In some embodiments, if the zero drift value of the differential pressure sensor is less than or equal to a preset threshold, the zero drift value is used to update the parameters of the Kalman differential pressure model to calibrate the Kalman differential pressure model to further improve the accuracy of the Kalman differential pressure model.

[0101] The present application provides a correction method for a differential pressure sensor, the method comprising obtaining a zero drift value of the differential pressure sensor, and if the zero drift value of the differential pressure sensor is greater than a preset threshold, using a pre-learned Kalman differential pressure model to obtain a theoretical value of the differential pressure sensor; and using the theoretical value to correct the differential pressure sensor to obtain a corrected value of the differential pressure sensor. It can be seen that in this method, the theoretical value of the Kalman differential pressure model is used to correct the value of the differential pressure sensor, thereby improving the accuracy of the differential pressure sensor and reducing the error of the differential pressure sensor.

[0102] The present application also provides a correction device for a differential pressure sensor, wherein the differential pressure sensor is used to detect the pressure difference between the upstream and downstream of the diesel particulate filter DPF of the post-treatment system, see Figure 2 , which is a schematic diagram of a device for identifying icing risk of a differential pressure sensor provided in an embodiment of the present application, the device comprising:

[0103] An acquisition unit 201 is used to acquire a zero drift value of the differential pressure sensor;

[0104] A comparison unit 202 is used to obtain a theoretical value of the differential pressure sensor by using a pre-learned Kalman differential pressure model if the zero drift value of the differential pressure sensor is greater than a preset threshold;

[0105] The correction unit 203 is used to correct the pressure difference sensor using the theoretical value to obtain a correction value of the pressure difference sensor.

[0106] In some possible implementations, the comparison unit 202 is further configured to update the parameters of the Kalman pressure difference model using the zero drift value if the zero drift value of the pressure difference sensor is less than or equal to the preset threshold.

[0107] In some possible implementations, the device further includes: a diagnosis unit;

[0108] The diagnosis unit is used to perform fault diagnosis based on the correction value of the differential pressure sensor.

[0109] In some possible implementations, the equation of the Kalman pressure difference model is:

[0110] Δp=AρV 2 +BμV+Cg(x)

[0111] Wherein, △p is the theoretical value of the differential pressure sensor, ρ is the exhaust density of the vehicle, V is the exhaust volume flow of the vehicle, μ is the exhaust dynamic viscosity coefficient of the vehicle, g(x) is the zero drift value of the differential pressure sensor, and A, B and C are coefficients.

[0112] In some possible implementations, the A, the B, and the C are determined in the following manner:

[0113] θ=[A,B,C] T

[0114] φ=[ρV 2 ,μV,g(x)] T

[0115] Δp mk =φ T θ k +v

[0116] θ k =θ k-1 +w

[0117] Among them, v and w are external disturbances, and K is a parameter of the Kalman pressure difference model.

[0118] The present application provides a correction device for a differential pressure sensor, wherein the device comprises an acquisition unit for acquiring a zero drift value of the differential pressure sensor; a comparison unit for obtaining a theoretical value of the differential pressure sensor using a pre-learned Kalman differential pressure model if the zero drift value of the differential pressure sensor is greater than a preset threshold; and a correction unit for correcting the differential pressure sensor using the theoretical value to obtain a corrected value of the differential pressure sensor. It can be seen that in the device, the theoretical value of the Kalman differential pressure model is used to correct the value of the differential pressure sensor, thereby improving the accuracy of the differential pressure sensor and reducing the error of the differential pressure sensor.

[0119] The present application also provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and the computer program is used to execute any one of the methods described in the method embodiments.

[0120] The present application also provides a device, which includes: a memory and a processor; a computer program is stored in the memory; and the processor is used to execute the computer program in the memory to implement any one of the methods described in the method embodiments.

[0121] Through the description of the above implementation methods, technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0122] In the several embodiments provided in this embodiment, it should be understood that the disclosed processing equipment and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0123] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] In addition, each functional unit in each embodiment of the present embodiment may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0125] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this embodiment is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform all or part of the steps of the method described in each embodiment. The aforementioned storage medium includes: flash memory, mobile hard disk, read-only memory, random access memory, disk or optical disk and other media that can store program codes.

[0126] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A correction method for a differential pressure sensor, characterized in that: The differential pressure sensor is used to detect the pressure difference between the upstream and downstream of the diesel particulate filter DPF of the post-treatment system, and the method includes: Obtaining a zero drift value of the differential pressure sensor; If the zero drift value of the differential pressure sensor is greater than a preset threshold, the theoretical value of the differential pressure sensor is obtained by using the pre-learned Kalman differential pressure model; Correcting the differential pressure sensor using the theoretical value to obtain a corrected value of the differential pressure sensor; If the zero drift value of the differential pressure sensor is less than or equal to the preset threshold, the parameters of the Kalman differential pressure model are updated using the zero drift value; The equation of the Kalman pressure difference model is: Δp=AρV 2 +BμV+Cg(x) Wherein, △p is the theoretical value of the differential pressure sensor, ρ is the exhaust density of the vehicle, V is the exhaust volume flow rate of the vehicle, μ is the exhaust dynamic viscosity coefficient of the vehicle, g(x) is the zero drift value of the differential pressure sensor, and A, B and C are coefficients; The A, B and C are determined in the following manner: θ=[A,B,C] T φ=[ρV 2 ,μV,g(x)] T Δp mk =φ T i k +v i k =θ k-1 +w In which, let the prior estimate be The posterior estimate is v and w are external disturbances, and K is a parameter of the Kalman pressure difference model.

2. The method according to claim 1, characterized in that: The method further comprises: Fault diagnosis is performed based on the correction value of the differential pressure sensor.

3. A correction device for a differential pressure sensor, characterized in that: The differential pressure sensor is used to detect the pressure difference between the upstream and downstream of the diesel particulate filter DPF of the post-treatment system, and the device includes: An acquisition unit, used for acquiring a zero drift value of the differential pressure sensor; A comparison unit, configured to obtain a theoretical value of the differential pressure sensor by using a pre-learned Kalman differential pressure model if the zero drift value of the differential pressure sensor is greater than a preset threshold; a correction unit, configured to correct the differential pressure sensor using the theoretical value to obtain a correction value of the differential pressure sensor; The comparison unit is further configured to update the parameters of the Kalman pressure difference model using the zero drift value if the zero drift value of the pressure difference sensor is less than or equal to the preset threshold value; The equation of the Kalman pressure difference model is: Δp=AρV 2 +BμV+Cg(x) Wherein, △p is the theoretical value of the differential pressure sensor, ρ is the exhaust density of the vehicle, V is the exhaust volume flow rate of the vehicle, μ is the exhaust dynamic viscosity coefficient of the vehicle, g(x) is the zero drift value of the differential pressure sensor, and A, B and C are coefficients; The A, B and C are determined in the following manner: θ=[A,B,C] T φ=[ρV 2 ,μV,g(x)] T Δp mk =φ T i k +v i k =θ k-1 +w In which, let the prior estimate be The posterior estimate is v and w are external disturbances, and K is a parameter of the Kalman pressure difference model.

4. The device according to claim 3, characterized in that The device further comprises: a diagnostic unit; The diagnosis unit is used to perform fault diagnosis based on the correction value of the differential pressure sensor.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method according to any one of claims 1 to 3.

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