Commercial vehicle DPF no-load pressure difference self-learning correction method

Abstract

The invention relates to the technical field of diesel vehicle exhaust treatment control, in particular to a commercial vehicle DPF no-load pressure difference self-learning correction method which comprises the following steps: (1) constructing an air pressure difference MAP: establishing a three-dimensional mapping relation taking exhaust flow and exhaust temperature as input variables and taking no-load pressure difference as an output value; (2) when the vehicle driving mileage is smaller than a preset mileage threshold value and the DPF carbon load is smaller than a preset load threshold value, a self-learning strategy is activated; (3) dividing operation condition groups based on the real-time exhaust flow and the exhaust temperature, and screening micro-condition points; (4) the real-time deviation between the actually-measured pressure difference of the DPF and the theoretical pressure difference value output by the air pressure difference MAP is calculated, and the multiple deviation values of the same micro-working condition are averaged; and (5) feeding back the averaged deviation to the air pressure difference MAP for dynamic correction.According to the commercial vehicle DPF no-load pressure difference self-learning correction method, the output value of the air pressure difference MAP after self-learning correction can be tightly attached to the actual measurement value, and the accuracy of a carbon load model is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of diesel vehicle exhaust treatment control, and in particular to a commercial vehicle DPF no-load pressure difference self-learning correction method. Background Art

[0002] As national regulations tighten the control of pollutant emissions from automotive diesel engines, all current National VI diesel vehicles must be equipped with a particulate filter (DPF) to capture carbon particles produced by diesel combustion. When the carbon particles captured in the DPF reach a certain weight, thermal regeneration is required to empty the DPF, that is, to burn these carbon particles at high temperatures to restore its capture function. The engine electronic control unit (ECU) uses an internally stored control algorithm to estimate the weight of the carbon particles in the DPF (referred to as carbon load) in real time to ensure that the vehicle can accurately trigger the DPF regeneration function. Therefore, the accuracy of the ECU's carbon load model value is crucial. Once the deviation is large, the DPF will not be able to regenerate in time and become clogged, or frequent regeneration will cause engine oil dilution, seriously damaging the engine and causing serious economic consequences for users.

[0003] The ECU's control algorithm is based on a carbon load model value estimated by differential pressure sensors installed at the DPF inlet and outlet. Currently, the common control algorithm subtracts the differential pressure caused by carbon particles from the differential pressure sensor's actual value. This difference is then used to generate a model value through a series of calculations. The accuracy of the DPF's differential pressure significantly impacts the final model value.

[0004] The current mainstream DPF substrate is a porous cylinder made of fired silicon carbide or cordierite. Its cross-section is dotted with hundreds of channels (one end sealed and the other open). Gas flow through these channels creates resistance, which is the main reason for the DPF's inherent pressure differential. Due to manufacturing errors during the production of these substrates, and the lack of complete consistency after packaging into the exhaust pipe, each DPF assembly is not tested for pressure differential deviation after it rolls off the production line. If the pressure differential of the produced DPF significantly exceeds the test value during the development process, it will result in the serious economic consequences mentioned above.

[0005] Therefore, in view of the above situation, there is an urgent need to develop a commercial vehicle DPF no-load pressure difference self-learning correction method to overcome the shortcomings in current practical applications. Summary of the Invention

[0006] The object of the present invention is to provide a commercial vehicle DPF no-load pressure difference self-learning correction method to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A commercial vehicle DPF no-load pressure difference self-learning correction method includes the following steps:

[0009] (1) Constructing air pressure difference MAP: Establishing a three-dimensional mapping relationship with exhaust flow and exhaust temperature as input variables and no-load pressure difference as output value;

[0010] (2) Activation of self-learning conditions: When the vehicle mileage is less than a preset mileage threshold and the DPF carbon load is less than a preset load threshold, the self-learning strategy is activated;

[0011] (3) Operating condition classification and micro-operating condition screening: Divide the operating condition groups based on the real-time exhaust flow rate and exhaust temperature, and screen the micro-operating condition points;

[0012] (4) Pressure differential deviation calculation: Calculate the real-time deviation between the DPF measured pressure differential and the theoretical pressure differential output by the air pressure differential MAP, and average the multiple deviation values ​​under the same micro-operating condition;

[0013] (5) Dynamic correction of air pressure difference MAP: Feedback the deviation after averaging to the air pressure difference MAP to adjust the MAP value of the corresponding operating area.

[0014] As a further solution of the present invention: in step (2), the preset mileage threshold is 1000 km, which is used to eliminate the influence of DPF ash deposition on the air pressure difference.

[0015] As a further solution of the present invention: in step (2), the preset load threshold is 5g, which is used to ensure that there is no carbon soot accumulation inside the DPF.

[0016] As a further solution of the present invention: step (3) specifically includes:

[0017] The engine operating conditions are divided into low flow section, medium flow section and high flow section according to the exhaust flow rate. The three flow sections are low, medium and high operating condition groups respectively.

[0018] The exhaust flow rate and exhaust temperature data are collected in real time. When the exhaust flow rate change rate |ΔD| is less than 0.2 and the exhaust temperature change |ΔT| is less than 5°C for 5 consecutive time periods, it is determined to be a micro-operating condition.

[0019] As a further solution of the present invention: the working condition group classification standard is:

[0020] Low flow section: exhaust flow ≤ 50kg / h;

[0021] Medium flow range: 50kg / h<exhaust flow≤150kg / h;

[0022] High flow section: exhaust flow>150kg / h.

[0023] As a further solution of the present invention: the calculation formula of the exhaust flow rate change rate ΔD is:

[0024]

[0025] Where d1 and d2 are the exhaust flow values ​​at adjacent time points, and t1 and t2 are the corresponding times;

[0026] The calculation formula of the exhaust temperature change ΔT is:

[0027] ΔT = T2 - T1;

[0028] Wherein, T1 and T2 are the exhaust temperature values ​​at adjacent time points.

[0029] As a further solution of the present invention: the exhaust flow rate and exhaust temperature of the micro-operating condition are taken as the arithmetic average of 5 consecutive data.

[0030] As a further solution of the present invention: in step (4), the mean value processing is to take the arithmetic mean value of the deviation values ​​of the same micro-operating condition for 5 consecutive times.

[0031] As a further solution of the present invention: in step (5), the correction range of the air pressure difference MAP is the entire area of ​​the operating condition group to which the current micro-operating condition belongs.

[0032] As a further solution of the present invention: Steps (3) to (5) are executed cyclically in the ECU with a period of 100 ms.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. Improve user operating quality: Reduce the probability of DPF-related after-sales problems, avoid DPF blockage or frequent regeneration caused by excessive deviation of carbon load model values, thereby preventing engine oil dilution and serious damage, and reducing user economic losses;

[0035] 2. Zero hardware cost upgrade: Without increasing hardware costs, the air pressure difference consistency issue between mass-produced DPF assemblies and development samples can be solved solely through the pressure difference self-learning strategy;

[0036] 3. Significant correction effect: For DPF assemblies with large air pressure difference deviation, the air pressure difference MAP output value can closely match the actual measurement value after self-learning correction, ensuring the accuracy of the carbon load model;

[0037] 4. Full working condition coverage correction: Make overall adjustments to the air pressure difference MAP values ​​of all micro-working condition areas within the same working condition group to ensure correction integrity;

[0038] 5. Real-time dynamic response: The self-learning strategy is executed in the ECU in a 100ms cycle to achieve high real-time pressure difference correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a framework diagram of the DPF no-load pressure difference self-learning strategy in an embodiment of the present invention.

[0040] Figure 2 This is a logic flow chart of the DPF no-load pressure difference self-learning strategy in an embodiment of the present invention.

[0041] Figure 3 This is a classification distribution diagram of engine operating conditions in an embodiment of the present invention.

[0042] Figure 4 This is a comparison chart of measured air pressure difference data of different DPF assemblies in an embodiment of the present invention.

[0043] Figure 5 This is a diagram verifying the effect of the DPF no-load pressure difference self-learning correction in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0046] See also Figure 1-Figure 5 The embodiment of the present invention provides a commercial vehicle DPF no-load pressure difference self-learning correction method, comprising the following steps:

[0047] (1) Constructing air pressure difference MAP: Establishing a three-dimensional mapping relationship with exhaust flow and exhaust temperature as input variables and no-load pressure difference as output value;

[0048] (2) Activation of self-learning conditions: When the vehicle mileage is less than a preset mileage threshold and the DPF carbon load is less than a preset load threshold, the self-learning strategy is activated;

[0049] (3) Operating condition classification and micro-operating condition screening: Divide the operating condition groups based on the real-time exhaust flow rate and exhaust temperature, and screen the micro-operating condition points;

[0050] (4) Pressure differential deviation calculation: Calculate the real-time deviation between the DPF measured pressure differential and the theoretical pressure differential output by the air pressure differential MAP, and average the multiple deviation values ​​under the same micro-operating condition;

[0051] (5) Dynamic correction of air pressure difference MAP: Feedback the deviation after averaging to the air pressure difference MAP to adjust the MAP value of the corresponding operating area.

[0052] In one embodiment of the present invention, see Figure 1-Figure 5 In step (2), the preset mileage threshold is 1000 km, which is used to eliminate the influence of DPF ash deposition on air pressure difference.

[0053] In step (2), the preset load threshold is 5 g, which is used to ensure that there is no soot accumulation inside the DPF.

[0054] In one embodiment of the present invention, see Figure 1-Figure 5 , step (3) specifically includes:

[0055] The engine operating conditions are divided into low flow section, medium flow section and high flow section according to the exhaust flow rate. The three flow sections are low, medium and high operating condition groups respectively.

[0056] The exhaust flow rate and exhaust temperature data are collected in real time. When the exhaust flow rate change rate |ΔD| is less than 0.2 and the exhaust temperature change |ΔT| is less than 5°C for 5 consecutive time periods, it is determined to be a micro-operating condition.

[0057] The classification criteria for the working condition groups are:

[0058] Low flow section: exhaust flow ≤ 50kg / h;

[0059] Medium flow range: 50kg / h<exhaust flow≤150kg / h;

[0060] High flow section: exhaust flow>150kg / h.

[0061] The calculation formula of the exhaust flow rate change rate ΔD is:

[0062]

[0063] Where d1 and d2 are the exhaust flow values ​​at adjacent time points, and t1 and t2 are the corresponding times;

[0064] The calculation formula of the exhaust temperature change ΔT is:

[0065] ΔT = T2 - T1;

[0066] Wherein, T1 and T2 are the exhaust temperature values ​​at adjacent time points.

[0067] The exhaust flow rate and exhaust temperature of the micro-operating condition are the arithmetic average of 5 consecutive data.

[0068] In one embodiment of the present invention, see Figure 1-Figure 5In step (4), the mean value processing is to take the arithmetic mean of the deviation values ​​of the same micro-operating condition for 5 consecutive times.

[0069] In step (5), the correction range of the air pressure difference MAP is the entire area of ​​the operating condition group to which the current micro-operating condition belongs.

[0070] Steps (3) to (5) are executed in a loop in the ECU with a period of 100ms.

[0071] Specifically, the commercial vehicle DPF no-load pressure difference self-learning correction method of the present invention can correct the accuracy and consistency of the DPF's own pressure difference. The specific method is as follows:

[0072] The present invention refers to the original calculation signals in the engine ECU control logic, such as engine exhaust flow, exhaust temperature, mileage and carbon load, which will not be described in detail in this article;

[0073] like Figure 1 As shown in the figure, the MAP value is obtained by engineers through special tests during the vehicle development stage. The air pressure difference MAP is a three-dimensional graph. The X-axis inputs the exhaust flow, the Y-axis inputs the exhaust temperature, and the Z-axis value is the output value of the air pressure difference (pressure difference 1). The pressure difference 1 is input into the self-learning strategy module, and after a series of operations, the pressure difference correction value is obtained, which is returned to the air pressure difference MAP to correct the consistency between the mass-produced DPF assembly air pressure difference and the pressure difference of the sample used in the test development. Among them, the self-learning strategy module (i.e., the above-mentioned working condition classification and micro-working condition screening (step (3), pressure difference deviation calculation (step (4) and air pressure difference MAP dynamic correction (step (5))) is calculated in the ECU according to a cycle of 100ms.

[0074] like Figure 2 As shown, the calculation conditions of the self-learning strategy module are as follows: First, it is necessary to determine whether the vehicle is in a near-new state, considering that vehicles with long mileage may have ash deposits in the DPF that affect the accuracy of the air pressure difference; second, the carbon load monitored by the ECU must be less than 5g, which means that the DPF is clean and the collected soot is very small, which has no impact on the air pressure difference. The self-learning strategy can only be activated when both conditions are met;

[0075] If both conditions are met, it means that the self-learning function can be performed. First, the operating conditions obtained based on the exhaust flow and exhaust temperature are roughly divided into three categories, such as Figure 3 As shown in the figure, they are operating condition group 1, operating condition group 2 and operating condition group 3. Operating condition group 1 is the low exhaust flow section, operating condition group 2 is the medium exhaust flow section, and operating condition group 3 is the high exhaust flow section. Each operating condition group may have multiple micro-operating condition distributions, such as Figure 3Medium yellow circle. Based on the exhaust temperature and exhaust flow currently monitored by the ECU, the operating condition screening combination is performed to determine which operating condition group the operating point is in, and then clustered into one or more micro-operating condition points, which then enter their respective pressure difference deviation calculation modules;

[0076] The operating condition screening strategy is to select one or more micro-operating conditions based on the time distribution characteristics of the exhaust temperature and exhaust flow currently monitored by the ECU. The ECU will store the exhaust temperature and exhaust flow data for the 10 time periods before the current time in real time. The screening principle is as follows:

[0077] The ECU calculates the exhaust flow rate change rate ΔD and exhaust temperature drop ΔT in real time using the following formula:

[0078]

[0079] Among them, d2 and d1 are the exhaust flow values ​​at adjacent time points, t2 and t1 are the corresponding times; T2 and T1 are the exhaust temperature values ​​at adjacent time points.

[0080] If |ΔD|<0.2 and |ΔT|<5°C for five consecutive time periods, it is determined to be a micro-operating condition, and the average value of the exhaust flow rate and exhaust temperature for five consecutive times is used as the exhaust flow rate and exhaust temperature coordinates of the micro-operating condition.

[0081] The logic of the pressure differential deviation calculation module is to calculate the difference between the actual pressure differential obtained by the DPF pressure differential sensor and the real-time calculated pressure differential 1, and input it into the pressure differential deviation calculation module. Based on the described micro-operating condition, the deviation under the corresponding micro-operating condition is obtained and recorded. After recording five times, the average value is taken as the output value of the air pressure differential deviation under that micro-operating condition. The execution example is shown below (t is time, Δpxx is the air pressure differential deviation):

[0082] t1: Micro-condition 2 of operating condition group 2 is detected, Δp02 is recorded and stored in the ECU memory;

[0083] t2: Micro-condition 1 of operating condition group 2 is detected, Δp01 is recorded and stored in the ECU memory;

[0084] t3: Micro-condition 1 of operating condition group 2 is detected, Δp11 is recorded and stored in the ECU memory;

[0085] t4: Micro-condition 1 of operating condition group 2 is detected, Δp21 is recorded and stored in the ECU memory;

[0086] t5: Micro-condition 1 of operating condition group 2 is detected, Δp31 is recorded and stored in the ECU memory;

[0087] t6: Micro-condition 1 of operating condition group 2 is detected, Δp41 is recorded and stored in the ECU memory;

[0088] t6: Micro-condition 1 of operating condition group 2 is detected, Δp51 is recorded and stored in the ECU memory;

[0089] t7: Micro-condition 2 of operating condition group 2 is detected, Δp02 is recorded and stored in the ECU memory;

[0090] At this time, the five deviations of micro-condition 1 are collected, and the air pressure difference deviation of this condition can be obtained according to the following formula:

[0091]

[0092] The air pressure difference deviation calculated above is the difference between the no-load pressure difference of the DPF installed on the vehicle and the DPF sample used in the vehicle development stage;

[0093] If the air pressure difference deviation exceeds a certain limit requirement, it will be fed back to Figure 2 In the air pressure differential MAP shown, otherwise no feedback will be given, and the DPF sample consistency is considered good. Assuming that the deviation values ​​obtained from multiple micro-operating conditions selected in the operating condition group X will adjust the values ​​of the air pressure differential MAP in the area within the operating condition group X.

[0094] In summary, the present invention's commercial vehicle DPF no-load pressure differential self-learning correction method can improve the operating quality of vehicles using DPFs and reduce the probability of DPF-related after-sales problems. Without increasing hardware costs, the problem is solved by simply incorporating a pressure differential self-learning strategy.

[0095] Table 1 shows three different DPF assemblies. 1# is the test sample used during vehicle development. In order to verify the effectiveness of this strategy, two samples with significant differences from 1# were selected. 2# is the sample with the largest air pressure difference selected, and 3# is the sample with the smallest air pressure difference selected. Partial air pressure difference data of the three groups of samples were measured on the vehicle, as shown below. Figure 4 shown.

[0096] Table 1 Three groups of different DPF pressure difference measured data results

[0097]

[0098] After using the self-learning strategy, the engine ECU successfully completed the self-learning of the 2# and 3# samples. The corrected air pressure difference results are as follows: Figure 5 After using the self-learning strategy, it can fit the measured results very well.

[0099] It should be noted that, in the present invention, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A commercial vehicle DPF no-load pressure difference self-learning correction method, characterized in that: The following steps are involved: (1) Constructing air pressure difference MAP: Establishing a three-dimensional mapping relationship with exhaust flow and exhaust temperature as input variables and no-load pressure difference as output value; (2) Activation of self-learning conditions: When the vehicle mileage is less than a preset mileage threshold and the DPF carbon load is less than a preset load threshold, the self-learning strategy is activated; (3) Operating condition classification and micro-operating condition screening: Divide the operating condition groups based on the real-time exhaust flow rate and exhaust temperature, and screen the micro-operating condition points; (4) Pressure differential deviation calculation: Calculate the real-time deviation between the DPF measured pressure differential and the theoretical pressure differential output by the air pressure differential MAP, and average the multiple deviation values ​​under the same micro-operating condition; (5) Dynamic correction of air pressure difference MAP: Feedback the deviation after averaging to the air pressure difference MAP to adjust the MAP value of the corresponding operating area.

2. The commercial vehicle DPF no-load pressure difference self-learning correction method according to claim 1 is characterized in that: In step (2), the preset mileage threshold is 1000 km, which is used to eliminate the influence of DPF ash deposition on the air pressure difference.

3. The commercial vehicle DPF no-load pressure difference self-learning correction method according to claim 1 is characterized in that: In step (2), the preset load threshold is 5 g, which is used to ensure that there is no soot accumulation inside the DPF.

4. The commercial vehicle DPF no-load pressure difference self-learning correction method according to claim 1, characterized in that: Step (3) specifically includes: The engine operating conditions are divided into low flow section, medium flow section and high flow section according to the exhaust flow rate. The three flow sections are low, medium and high operating condition groups respectively. The exhaust flow rate and exhaust temperature data are collected in real time. When the exhaust flow rate change rate |ΔD| is less than 0.2 and the exhaust temperature change |ΔT| is less than 5°C for 5 consecutive time periods, it is determined to be a micro-operating condition.

5. The commercial vehicle DPF no-load pressure difference self-learning correction method according to claim 4 is characterized in that: The classification criteria for the working condition groups are: Low flow section: exhaust flow ≤ 50kg / h; Medium flow range: 50kg / h<exhaust flow≤150kg / h; High flow section: exhaust flow>150kg / h.

6. The commercial vehicle DPF no-load pressure difference self-learning correction method according to claim 4, characterized in that: The calculation formula of the exhaust flow rate change rate ΔD is: Where d1 and d2 are the exhaust flow values ​​at adjacent time points, and t1 and t2 are the corresponding times; The calculation formula of the exhaust temperature change ΔT is: ΔT = T2 - T1; Among them, T1 and T2 are the exhaust temperature values ​​at adjacent time points.

7. The commercial vehicle DPF no-load pressure difference self-learning correction method according to claim 4, characterized in that: The exhaust flow rate and exhaust temperature of the micro-operating condition are the arithmetic average of 5 consecutive data.

8. The commercial vehicle DPF no-load pressure difference self-learning correction method according to claim 1, characterized in that: In step (4), the mean value processing is to take the arithmetic mean value of the deviation values ​​of the same micro-operating condition for 5 consecutive times.

9. The commercial vehicle DPF no-load pressure difference self-learning correction method according to claim 1, characterized in that: In step (5), the correction range of the air pressure difference MAP is the entire area of ​​the operating condition group to which the current micro-operating condition belongs.

10. The commercial vehicle DPF no-load pressure difference self-learning correction method according to claim 1, characterized in that: Steps (3) to (5) are executed in a loop in the ECU with a period of 100ms.

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