Method and computer program product for diagnosing a particle filter

Abstract

The invention relates to a method and a computer program product for identifying an absent or defective particle filter in an exhaust gas treatment system of an internal combustion engine, in particular a petrol engine, wherein in order to monitor the particle filter a pressure difference between the inlet and the outlet of the particle filter is measured and evaluated. In that context, it is provided that, depending on the operating parameters of the internal combustion engine and/or of the exhaust gas treatment system, a correlation of the measured pressure difference across the particle filter compared to an expected pressure difference for an intact reference particle filter, or a correlation of the gradient, with respect to time, of the measured pressure difference to an expected gradient, with respect to time, of the expected pressure difference for an intact reference particle filter, is determined, and that, in the event of high correlation it is concluded that a particle filter is present and intact, and in the event of low correlation it is concluded that a particle filter is absent or defective. The method makes it possible to identify an absent or defective particle filter under many operating conditions of the internal combustion engine, even in the case of very low absolute pressure differences, as is in particular the case with petrol particle filters.

Claims

1. A method for detecting a removed or defective particle filter (13) in an exhaust gas after-treatment system (16) of an internal combustion engine (10), wherein in order to monitor the particle filter (13) a differential pressure Δp (19) between the inlet and the outlet of the particle filter (13) is measured and evaluated, wherein a correlation of the measured differential pressure Δp (19) across the particle filter (13) with an expected differential pressure Δp* is evaluated for an intact reference particle filter when at least one of a group consisting of the measured differential pressure Δp (19), the expected differential pressure Δp*, and a first characteristic associated with the measured or expected differential pressure Δp, Δp* (19), exceed a respectively predefined first threshold value, a correlation of the time gradient d(Δp) of the measured differential pressure Δp (19) with an expected time gradient d(Δp*) of the expected differential pressure Δp* for an intact reference particle filter is evaluated when at least one of a group consisting of the gradient of the measured differential pressure Δp (19), the expected time gradient d(Δp*), and a further characteristic variable which is associated with the differential pressure Δp, Δp* (19), exceed a respectively predefined second threshold value, when a high correlation is evaluated for the time gradient or for the differential pressure, the intact particle filter (13) is present and operating, and when a low correlation is evaluated for the time gradient or for the differential pressure, the particle filter (13) has been removed or is defective.

2. The method as claimed in claim 1, wherein the expected differential pressure Δp* of the reference particle filter is determined in a model-like fashion as a function of at least one operating parameter of the internal combustion engine (10), an operating parameter of the exhaust gas after-treatment system (16), or both.

3. The method as claimed in claim 1, wherein the expected differential pressure Δp* or the expected time gradient d(Δp*) of the expected differential pressure Δp* is calculated from at least one selected from the group consisting of an exhaust volume flow, the time gradient of the exhaust gas volume flow, and a flow resistance of the intact reference particle filter, and in that a quadratic component of the volume flow is also taken into account during the calculation of the expected differential pressure Δp* or of the expected time gradient d(Δp*) of the expected differential pressure Δp*, which quadratic component takes into account the compression and expansion of the exhaust gas as the exhaust gas flows into the particle filter (13) and as the exhaust gas flows out of the particle filter (13).

4. The method as claimed in claim 1, wherein the measured differential pressure Δp (19) across the particle filter (13), the expected differential pressure Δp* across the reference particle filter, the volume flow, or a combination of the same are low-pass filtered in order to determine the expected differential pressure Δp*.

5. The method as claimed in claim 1, wherein in order to determine the respective correlation from the measured differential pressure Δp (19) and the expected differential pressure Δp* a first cross-correlation factor KKF.sub.1 is formed by means of a cross-correlation, and in that a second cross-correlation factor KKF.sub.2 is formed from the time gradient d(Δp) of the measured differential pressure d(Δp) (19) across the particle filter (13) and the expected time gradient d(Δp*) of the expected differential pressure Δp* across the reference particle filter by means of a cross-correlation.

6. The method as claimed in claim 5, wherein the first cross-correlation factor KKF.sub.1, the second cross-correlation factor KKF.sub.2, or both are each compared with a predefined threshold value, and when the respective threshold value is undershot a faulty particle filter (13) or the absence of a particle filter (13) is detected, and when the respective threshold value is reached or exceeded an installed and intact particle filter (13) is diagnosed.

7. The method as claimed in claim 1, wherein the differential pressure Δp (19), the time gradient d(Δp) of the measured differential pressure Δp (19), or both are determined from the signal of a differential pressure sensor (15) which is arranged above the particle filter (13) or from the signals of two differential pressure sensors or of two absolute pressure sensors which are arranged downstream and upstream of the particle filter (13) in the exhaust train (11), or from the difference between a measured absolute pressure at the inlet of the particle filter (13) and a modelled absolute pressure of the outlet of the particle filter (13) or from the difference between a measured relative pressure at the inlet of the particle filter (13) relative to the surroundings and a modelled relative pressure at the outlet of the particle filter (13) relative to the surroundings.

8. A vehicle for performing the method as claimed in claim 1, wherein the internal combustion engine is a gasoline-operated internal combustion engine (10) in which the exhaust gas system has at least one separate catalytic converter (12) and one of the particle filter (13) or a catalytic converter-particle filter combination or a catalytically coated particle filter (13).

9. A computer program containing instructions that when retrieved from a non-transitory computer readable medium and executed by a processor cause the processor to carry out the method claimed in claim 1.

10. The method as claimed in claim 1, wherein the particle filter (13) is continuously monitored.

11. A method for detecting a removed or defective particle filter (13) in an exhaust gas after-treatment system (16) of an internal combustion engine (10) by monitoring the particle filter (13), the method comprising: measuring a differential pressure Δp (19) between an inlet and an outlet of the particle filter (13), correlating the measured differential pressure Δp (19) across the particle filter (13) with an expected differential pressure Δp* is evaluated for an intact reference particle filter when at least one of a group consisting of the measured differential pressure Δp (19), the expected differential pressure Δp*, and a first characteristic associated with the measured or expected differential pressure Δp, Δp* (19) exceed a respectively predefined first threshold value, correlating a time gradient d(Δp) of the measured differential pressure Δp (19) with an expected time gradient d(Δp*) of the expected differential pressure Δp* for an intact reference particle filter when at least one of a group consisting of the gradient of the measured differential pressure Δp (19), the expected time gradient d(Δp*), and a further characteristic variable which is associated with the differential pressure Δp, Δp* (19), exceed a respectively predefined second threshold value, when a high correlation is evaluated for the time gradient and/or for the differential pressure, the particle filter (13) is present and operating, and when there is a low correlation for the time gradient and/or for the differential pressure, an indication is provided that the particle filter (13) has been removed or that the particle filter (13) is defective.

12. The method as claimed in claim 11, wherein the particle filter (13) is continuously monitored.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The invention will be explained in more detail below by means of an exemplary embodiment illustrated in the figures. In the drawing:

(2) FIG. 1 shows by way of example a technical environment in which the invention can be used,

(3) FIG. 2 shows a first schematic profile diagram of the differential pressure profiles for a measured reference differential pressure and a reference differential pressure which is determined by model, for an intact particle filter.

(4) FIG. 3 shows a second schematic profile diagram of the differential pressure profiles for the measured reference differential pressure and the reference differential pressure which is determined by means of a model, for a removed or defective particle filter, and

(5) FIG. 4 shows a third schematic profile diagram of the gradients of the differential pressure profiles for the measured reference differential pressure and the reference differential pressure which is determined by means of a model, for a removed or defective particle filter.

DETAILED DESCRIPTION

(6) FIG. 1 shows a schematic view of the technical environment in which the invention can be used. For example an internal combustion engine 10 with an exhaust gas after-treatment system 16 is illustrated. The internal combustion engine 10 is embodied as a gasoline engine. The exhaust gas of the internal combustion engine 10 is conducted away via an exhaust train 11. The exhaust gas after-treatment system 16, which is embodied in multiple stages in the exemplary embodiment shown, is arranged along the exhaust train 11. Viewed in the direction of flow of the exhaust gas (exhaust gas flow 14), firstly a catalytic converter 12 is provided, embodied here as a three-way catalytic converter. The catalytic converter 12 has a particle filter 13 connected downstream of it. In the selected basic illustration, the exhaust gas after-treatment system 16 has further components (not shown), such as exhaust gas probes and further sensors whose signals are fed to an engine controller (electronic control unit, ECU).

(7) In order to diagnose the particle filter 13, a differential pressure sensor 15 is provided by virtue of which the pressure difference (differential pressure 19) between a filter input and a filter output of the particle filter 13 can be determined. The output signal of the differential pressure sensor 15 is fed to a diagnostic unit 18 in which a diagnosis relating to a possibly broken, removed or blocked particle filter 13 can be carried out by means of on-board diagnostics (OBD). This diagnostic unit 18 can be part of the superordinate engine controller (ECU).

(8) The catalytic converter 12 and the particle filter 13 can also be connected together in the form of a four-way catalytic converter (FWC), that is to say a catalytically coated particle filter 13. It is also conceivable to determine the differential pressure 19 by means of two absolute pressure sensors which are arranged upstream and downstream of the particle filter 13. It is also possible to provide a differential pressure sensor respectively upstream and downstream of the particle filter 13, which differential sensors respectively measure the pressure in the exhaust train 11 in comparison with the ambient pressure. The differential pressure can also be determined from the difference between a measured absolute pressure at the inlet of the particle filter 13 and a modelled absolute pressure at the outlet of the particle filter 13. It is also conceivable to form the differential pressure from the difference between a measured relative pressure at the inlet of the particle filter 13 in comparison with the surroundings a modelled relative pressure at the outlet of the particle filter 13 in comparison with the surroundings.

(9) FIG. 2 and FIG. 3 each comprise schematic profile diagrams 20 of the differential pressure signals 21 for differential pressure profiles 22, 23 for a measured differential pressure Δp (measured differential pressure profile 23) and an expected differential pressure Δp* which is determined by means of a model (expected differential pressure profile 22) as a function of the time 24. The measured differential pressure Δp and therefore the measured differential pressure profile 23 are measured using the differential pressure sensor 15 shown in FIG. 1. The expected differential pressure Δp* and the time profile thereof are calculated from the absolute gas volume flow and the flow resistance of an intact reference particle filter.

(10) FIG. 2 shows by way of example the differential pressure profiles 22, 23 for an intact and installed particle filter 13. It is characteristic here that only small differences in signal level and phase differences occur between the modelled differential pressure profile 22 and the measured differential pressure profile 23. There is therefore a high degree of correlation between the two differential pressure profiles 22, 23. This relates to the absolute values of both the measured and expected differential pressure Δp, Δp* 19 as well as the time derivatives thereof.

(11) In contrast to FIG. 2, FIG. 3 shows, in the further profile diagram 20, the differential pressure profiles 22, 23 for the measured differential pressure Δp 19 and the expected differential pressure Δp* in the case of a removed or defective particle filter 13. Significant deviations in respect of the signal level and/or in respect of the phase occur between the differential pressure profiles 22, 23. There is therefore low correlation between the two differential pressure profiles 22, 23. The absolute values of both the measured and of the expected differential pressure Δp, Δp* 19 as well as the time derivatives thereof are affected by this.

(12) The inventive detection of a removed or defective particle filter 13, in particular gasoline particle filter, is based on the determination of the correlation of the measured differential pressure Δp 19 or of the time gradient d(Δp) of the measured differential pressure Δp 19 across the particle filter 13 with the expected differential pressure Δp or the expected time gradient d(Δp*) of the expected differential pressure Δp across an intact particle filter 13. The expected differential pressure Δp* and the expected time gradient d(Δp*) of the expected differential pressure Δp* are determined here from a model as a function of current operating variables of the internal combustion engine 10 and/or of the exhaust gas after-treatment system 16.

(13) If the particle filter 13 is installed correctly in the exhaust train 11, there is either a good correlation between the differential pressure Δp measured in a current measurement and the expected differential pressure Δp*, or in the case of dynamic excitation there is a good correlation between the measured time gradient d(Δp) of the measured differential pressure Δp 19 for the current measurement and the expected time gradient d(Δp*) of the expected differential pressure Δp*. If the particle filter 13 is, on the other hand, removed or defective, in each case there is a very weak correlation. Removal of the particle filter 13 or a defect therein can therefore be unambiguously detected.

(14) One advantage of this method is that, on the one hand, it evaluates, in contrast to known differential-pressure-based methods, not only the absolute pressure difference across the particle filter 13 but also the change therein over time. Therefore, even in the case of very low absolute pressure differences 19, it is possible to detect a removed or defective particle filter 13. The diagnostic method is robust here against offset tolerances of the differential pressure sensor 15. These offset tolerances impede all diagnostic methods which are based only on the absolute differential pressure. If there is sufficiently large measured differential pressures Δp and expected differential pressures Δp* 19 for which the offset tolerances of the predefined differential pressure sensor 15 are negligible, the removal of the particle filter 13 or the defect therein can be detected, in comparison, in a faster and more stable fashion by evaluating the correlation between the measured differential pressure Δp 19 and the differential pressure Δp* which is expected across an intact particle filter 13.

(15) The measured differential pressure signal Δp.sub.(k) is firstly low-pass filtered in order to suppress the noise. Subsequently, the time gradient d(Δp.sub.(K))/dk of the filtered differential pressure signal Δp.sub.(K) is determined, where k signifies the k-th measurement. In parallel with this, corresponding reference values for the expected differential pressure signal Δp*.sub.(k) and/or the expected time gradient d(Δp*.sub.(k))/dk of the expected differential pressure signal Δp*.sub.(k) are determined. For this purpose, a time profile of the expected differential pressure signal Δp*.sub.(k) or the time profile of the expected time gradient d(Δp*.sub.(k))/dk of the expected differential pressure signal Δp*.sub.(k) across an intact and installed particle filter 13 is calculated from an exhaust gas volume flow or from the time gradient thereof and the flow resistance of the intact particle filter 13, the reference filter. The expected values or the volume flow which is included in the determination of the expected values can optionally also be low-pass filtered.

(16) The expected differential pressure Δp can be calculated from the flow resistance A of the particle filter 13 and the exhaust gas volume flow dVol:
Δp=A*dVol

(17) In order to achieve a higher model accuracy it is advantageous to take into account a quadratic component b*dVol.sup.2. This partial differential pressure is caused by the compression and expansion of the exhaust gas when the exhaust gas flows into the particle filter or out of the particle filter.
Δp=A*dVol+b*dVol.sup.2

(18) In a subsequent step, it is determined, in each case by means of a standardized cross-correlation, to what extent the profile of the measured differential pressure signal Δp.sub.(k) corresponds to that of the expected differential pressure signal Δp*.sub.(k) and to what extent the profile of the measured time gradient d(Δp.sub.(k)) corresponds to the profile of the expected time gradient d(Δp*.sub.(k)). For this purpose, in each case a cross-correlation factor KKF is formed according to the following relationship:
KKF.sub.1=Σ(Δp.sub.(k)*Δp*.sub.(k))/Σ(Δp*.sub.(k)*Δp*.sub.(k))  (1)
KKF.sub.2=Σ(d(Δp.sub.(k))*d(Δp*.sub.(k)))/Σ(d(Δp*.sub.(k))*d(Δp*.sub.(k)))  (2)

(19) Where:

(20) Δp.sub.(k): signifies measured values of the measured differential pressure Δp 19

(21) Δp*.sub.(k): signifies calculated values of the expected differential pressure Δp*

(22) d(Δp.sub.(k)): signifies measured values of the measured gradient d(Δp) of the measured differential pressure Δp 19, and

(23) d(Δp*.sub.(k)): signifies calculated values of the expected gradient d(Δp*) of the expected differential pressure Δp*.

(24) In order to assess whether the particle filter 13 is correctly provided or installed and is functioning correctly, the respective output value of the standardized cross-correlation, the first cross-correlation factor KKF.sub.1 or the second cross-correlation factor KKF.sub.2 is compared with a previously defined threshold value which is stored in the control unit or in the diagnostic unit 18. If the result is below the threshold value, which corresponds to an only low correlation or even to no correlation at all, the particle filter 13 is removed or defective. If the result is above the threshold value, which corresponds to a good correlation, the particle filter 13 is present and intact.

(25) FIG. 4 shows third schematic profile diagram 20 of the differential pressure gradient signals 25 as a function of the time 24. For this purpose, the differential pressure gradient profiles 26, 27 are plotted for the measured differential pressure Δp and for the expected differential pressure Δp* 19 which is determined by means of a model. The measured differential pressure gradient profile 27, which is formed from the measured differential pressure Δp 19, deviates significantly from the expected differential pressure gradient profile 26 such as has been determined for an intact particle filter 13. In this context, a high dynamic excitation is present, which is apparent in the form of the high signal deflections of the expected differential pressure gradient profile 26. The second cross-correlation factor KKF.sub.2 will input correspondingly small values which lie below the predefined threshold value. Therefore, in the case of signal profiles according to FIG. 4 it is possible to assume a defective or removed particle filter 13.

(26) The evaluation about the absolute measured differential pressures Δp and expected differential pressures Δp* 19, that is to say about the first cross-correlation factor KKF.sub.1, occurs reliably if the absolute expected differential pressure Δp* across the intact particle filter 13 exceeds a predefined threshold. The evaluation about the measured time gradients d(Δp) and the expected time gradients d(Δp*) occurs reliably if a certain dynamic excitation is present, i.e. if the differential pressure gradients 26, 27 exceed a certain amount. Therefore, an evaluation about the second cross-correlation factor KKF.sub.2 occurs only when specific dynamic criteria are satisfied. The gradients of the exhaust gas mass flow, of the exhaust gas volume flow, of the rotational speed or of variables derived therefrom are possible for this. Ideally, the gradient of the differential pressure reference value is also used directly. The evaluation about the first cross-correlation factor KKF.sub.1 occurs when sufficiently large measured differential pressures Δp and/or expected differential pressures Δp* are present across the particle filter 13. Therefore, both in high dynamic travel situations and in operating situations which cause high exhaust gas mass flows and therefore high differential pressures Δp across the particle filter 13, a defective or removed particle filter 13 can be reliably detected.

(27) In one advantageous configuration, the diagnostic method is stored as software in the diagnostic unit 18 and can be used, in particular, in gasoline engines with future gasoline particle filters, but basically also in diesel engines with diesel particle filters.