GB2523990A - Method of controlling a diesel particulate filter - Google Patents

Abstract

A method of controlling a Diesel particulate filter (DPF) 282 of an internal combustion engine 110 during a soot loading phase after an engine shut-off. The method comprises: before engine cranking, determining if an engine shut-off time is larger than a first time threshold, and a DPF inlet temperature is smaller than a first temperature threshold; if so, determining a learning phase start (PELPS) if the engine is operating for more than a second time threshold; performing a learning phase (PEL) consisting of activating two timers and calculating a correction factor of an exhaust gas flow resistance as the average difference, during the learning phase, between a reference flow resistance, which is a function of a calculated soot loading value, and a current flow resistance, and correcting the current flow resistance with the correction factor; and determining a learning phase end (PELPE) if at least one of the two timers is larger than a corresponding time threshold.

Description

METHOD OF CONTROLLING A DIESEL PARTICULATE FILTER

TECHNICAL FIELD

The present disclosure relates to a method of controlling a Diesel particulate filter (DPF) of a Diesel engine. Particularly, the method is related to the identification of the DPF parking effect in order not to worsening the soot loading evaluation and is based on a learning logic to correctly perform the soot loading evaluation. The disclosure is also related to a computer program, implementing such a method.

BACKGROUND

It is known that modern engines are provided with one or more exhaust aftertreatment devices. The aftertreatment devices may be any device configured to change the composition of the exhaust gases, such as a diesel oxidation catalyst (DOC) located in the exhaust line for degrading residual hydrocarbons (HG) and carbon oxides (GO) contained in the exhaust gas, and a Diesel particulate filter (DPF) located in the exhaust line downstream the DOC, for capturing and removing diesel particulate matter (soot) from the exhaust gas In particular, the diesel particulate filter (DPF) collects liquid and solid particles in a porous substrate structure, while allowing exhaust gases to flow through. As it reaches its nominal storage capacity, needs to be cleaned by a process called regeneration. A DPF physical model returns a soot loading evaluation starting from the differential pressure signal read across the DPF by an exhaust gas pressure sensor (EGP). This is applied to the overall soot estimation strategy in order to optimize the DPF regeneration frequency.

A particular condition related to the estimation of the soot loading appears during a prolonged vehicle disuse, if the soot in the OFF reaches very low temperatures. This condition is well known as parking effect: it means that, when the vehicle is started again, even if the soot quantity is not changed in the trap, there is a significant pressure drop reduction across the filter, thus leading to a soot loading under-estimation by the DPF physical model, taking into account the information of the EGP.

A possible solution would be a strong reduction of the EGR rate, in order to minimize the parking effect. However, this will imply a strong impact on the emission (higher difficulty to achieve emission levels, above all according to new legislative regulations) and a huge calibration effort.

Therefore a need exists for a computer program implementing a method of controlling the DPF, which is able to catch the conditions leading to the parking effect phenomenon and define countermeasures to correctly estimated the soot loading.

An object of an embodiment of the invention is to provide a computer program implementing a method of controlling the DPF, which is reliable in identifying the parking condition and avoids soot loading under-prediction.

These objects are achieved by a computer product and by an automotive system having the features recited in the independent claims.

The dependent claims delineate preferred and/or especially advantageous aspects.

SUMMARY

An embodiment of the disclosure provides a computer program comprising a computer-code suitable for performing a method olcontrolling a Diesel particulate filter of an internal combustion engine, wherein the method controls a DPF soot loading phase after an engine shut-off, by carrying out the following steps: -before engine cranking, determining if an engine shut-off time is larger than a first time threshold and a DPF inlet temperature is smaller than a first temperature threshold; -determining a learning phase start if the engine is operating for more than a second time threshold; -performing a learning phase consisting in: o activating two timers, o calculating a correction factor of an exhaust gas flow resistance as the average difference, during said learning phase, between a reference flow resistance, which is a function of a calculated soot loading value, and a current flow resistance, and a correcting the current flow resistance with this correction factor; -determining a learning phase end if at least one of the two timers is larger than a corresponding time threshold.

An advantage of this embodiment is that all needed parameters for identifying the parking effect are defined. Furthermore, the method performs a learning logic to determines the soot loading under estimation, due to the parking effect. The learning logic compensates the pressure drop reduction of the EGP signal through a comparison between a reference flow resistance and the current flow re&stance of the exhaust gas, being the flow resistance values strictly correlated to the soot loading values. The correction factor, which is determined by such comparison is used to compensate the soot loading under estimation, due to the pressure drop reduction derived from the EGP sensor.

According to another embodiment, said soot loading value is a previous soot level estimated by a soot loading physical model.

An advantage of this embodiment is that the present strategy is mainly based on the soot loading physical model, which is more reliable than other back-up models.

According to a still further embodiment, said soot loading value is calculated as the sum of the previous soot level, estimated by the soot loading physical model, and an integrated soot rate, estimated by a soot loading statistical model.

Notwithstanding the previous statement about the reliability of the physical model, in case the learning logic takes a long time, the accumulated soot during this period cannot be neglected and therefore the soot loading value also takes into account the integrated soot rate estimated by a back-up model, for example a statistical model.

According to still another embodiment, the first timer starts if a DPF inlet temperature is larger than a second temperature threshold and ends if it is larger than a time threshold of about two minutes.

An advantage of this embodiment is that the method is able to define all parameters, establishing until when said parking effect strategy should be maintained. In particular, after a certain time the DPF inlet temperature is above a certain temperature threshold, the parking effect becomes negligible and the learning logic can be stopped.

According to a still further embodiment, the second timer starts at the same time of the learning phase start and ends if it is larger than a time threshold of about five minutes.

An advantage of this embodiment is that a second timer is foreseen, in order to limit the total duration of the learning phase in any condition.

According to another embodiment, a weighted time step is established for each of these two timers and is a function of a flow resistance reliability.

An advantage of this embodiment is that a weighted time step for the two timers, as a function of the flow resistance reliability, allows to increase the learning phase if most of the leaming time has been spent in low accurate regions.

Another embodiment of the disclosure provides an automotive system comprising an internal combustion engine and an electronic control unit, the engine comprising an aftertreatment system having at least a particulate filter wherein the electronic control unit is configured to perform the computer program according to any of the previous embodiments.

The method according to one of its aspects can be carried out with the help of a computer program comprising a program-code for carrying out all the steps of the method described above, and in the form of computer program product comprising the computer program.

The computer program product can be embedded in a control apparatus for an internal combustion engine, comprising an Electronic Control Unit (ECU), a data carrier associated to the ECU, and the computer program stored in a data carrier, so that the control apparatus defines the embodiments described in the same way as the method. In this case, when the control apparatus executes the computer program all the steps of the method described above are carried out.

BRIEF DESCRIPTION OF THE DRAWINGS

The various embodiments will now be described, by way of example, with reference to the accompanying drawings, in which: Figure 1 shows an automotive system.

Figure 2 is a section of an internal combustion engine belonging to the automotive system of figure 1.

Figure 3 is a schematic view of the aftertreatment system according to the invention.

Figure 4 is a scheme representing the parking effect.

Figure 5 is a graph depicting the soot level under-estimation due to the parking effect.

Figure 6 is a graph depicting the behavior of the soot toading physical model due to the parking effect.

Figure 7 is a flowchart of the computer program according to an embodiment of the present invention.

Figure 8 is a block diagram of the learning logic according to another embodiment of the present invention Figure 9 is a block diagram of the learning logic end according to still another embodiment of the present invention

DETAILED DESCRIPTION OF THE DRAWINGS

Some embodiments may include an automotive system 1001 as shown in Figures 1 and 2, that includes an internal combustion engine (ICE) 110 having an engine block 120 defining at least one cylinder 125 having a piston 140 coupled to rotate a crankshaft 145. A cylinder head 130 cooperates with the piston 140 to define a combustion chamber 150.

A fuel and air mixture (not shown) is disposed in the combustion chamber 150 and ignited, resulting in hot expanding exhaust gasses causing reciprocal movement of the piston 140.

The fuel is provided by at least one fuel injector 160 and the air through at least one intake port 210. The fuel is provided at high pressure to the fuel injector 160 from a fuel rail 170 in fluid communication with a high pressure fuel pump 1 80 that increase the pressure of the fuel received from a fuel source 190.

Each of the cylinders 125 has at least two valves 2151 actuated by a camshaft 135 rotating in time with the crankshaft 145. The valves 215 selectively allow air into the combustion chamber 150 from the port 210 and alternately allow exhaust gases to exit through a port 220. In some examples, a cam phaser 155 may selectively vary the timing between the camshaft 135 and the crankshaft 145.

The air may be distributed to the air intake port(s) 210 through an intake manifold 200. An air intake duct 205 may provide air from the ambient environment to the intake manifold 200. In other embodiments, a throttle body 330 may be provided to regulate the flow of air into the manifold 200. In still other embodiments, a forced air system such as a turbocharger 230, having a compressor 240 rotationally coupled to a turbine 250, may be provided. Rotation of the compressor 240 increases the pressure and temperature of the air in the duct 205 and manifold 200. An intercooler 260 disposed in the duct 205 may reduce the temperature of the air. The turbine 250 rotates by receMng exhaust gases from an exhaust manifold 225 that directs exhaust gases from the exhaust ports 220 and through a series of vanes prior to expansion through the turbine 250. The exhaust gases exit the turbine 250 and are directed into an exhaust system 270. This example shows a fixed geometry turbine 250 including a waste gate 290. In other embodiments, the turbocharger 230 may be a variable geometry turbine (VGT) with a VGT actuator arranged to move the vanes to alter the flow of the exhaust gases through the turbine.

The exhaust system 270 may include an exhaust pipe 275 having one or more exhaust aftertreatment devices 280. The aftertreatment devices may be any device configured to change the composition of the exhaust gases. Some examples of aftertreatment devices a 280 include, but are not limited to, catalytic converters (two and three way), oxidation catalysts or lean NOx traps, particulate filter 282, selective catalytic reduction (SCR) systems. Other embodiments may include an exhaust gas recirculation (EGR) system 300 coupled between the exhaust manifold 225 and the intake manifold 200. The EGR system 300 may include an EGR cooler 310 to reduce the temperature of the exhaust gases in the EGR system 300. An EGR valve 320 regulates a flow of exhaust gases in the EGR system 300.

The automotive system 100 may further include an electronic control unit (ECU) 450 in communication with one or more sensors and/or devices associated with the ICE 110 and equipped with a data carrier 40. The ECU 450 may receive input signals from various sensors configured to generate the signals in proportion to various physical parameters associated with the ICE 110. The sensors include, but are not limited to, a mass airflow, pressure, temperature sensor 340, a manifold pressure and temperature sensor 350, a combustion pressure sensor 360, coolant and oil temperature and level sensors 380, a fuel rail pressure sensor 400, a cam position sensor 410, a crank position sensor 420, exhaust pressure and temperature sensors 430, an EGR temperature sensor 440, and an accelerator pedal position sensor 445. Furthermore, the ECU 450 may generate output signals to various control devices that are arranged to control the operation of the ICE 110, including, but not limited to, the fuel injectors 160, the throttle body 330, the EGR Valve 320, the waste gate actuator 290, and the cam phaser 155. Note, dashed lines are used to indicate communication between the ECU 450 and the various sensors and devices, but some are omitted for clarity.

Turning now to the ECU 450, this apparatus may include a digital central processing unit (CPU) in communication with a memory system and an interface bus. The CPU is configured to execute instructions stored as a program in the memory system, and send and receive signals to/from the interface bus. The memory system may include various storage types including optical storage, magnetic storage, solid state storage, and other non-volatile memory. The interface bus may be configured to send, receive, and modulate analog and/or digital signals to/from the various sensors and control devices. The program may embody the methods disclosed herein, allowing the CPU to carryout out the steps of such methods and control the ICE 110.

The program stored in the memory system is transmitted from outside via a cable or in a wireless fashion. Outside the automotive system 100 it is normally visible as a computer program product, which is also called computer readable medium or machine readable medium in the art, and which should be understood to be a computer program code residing on a carrier, said carrier being transitory or non-transitory in nature with the consequence that the computer program product can be regarded to be transitory or non-transitory in nature.

An example of a transitory computer program product is a signal, e.g. an electromagnetic signal such as an optical signal, which is a transitory carrier for the computer program code. Carrying such computer program code can be achieved by modulating the signal by a conventional modulated technique such as QPSK for digital data, such that binary data representing said computer program code is impressed on the transitory electromagnetic signal. Such signals are e.g. made use of when transmitting computer program code in a wireless fashion via a WiFi connection to a laptop.

In case of a non-transitory computer program product the computer program code is embodied in a tangible storage medium. The storage medium is then the non-transitory carrier mentioned above, such that the computer program code is permanently or non-permanently stored In a retrievable way in or on this storage medium. The storage medium can be of conventional type known in computer technology such as a flash memory, an Asic, a CD or the like.

Instead of an ECU 450, the automotive system 100 may have a different type of processor to provide the electronic logic, e.g. an embedded controller, an onboard computer, or any processing module that might be deployed in the vehicle.

The diesel particulate filter (DPF) 282 collects liquid and solid particles in a porous substrate structure, while allowing exhaust gases to flow through. As it reaches its nominal storage capacity, needs to be cleaned by a process called regeneration, during which the exhaust gas temperature is increased substantially to create a condition, whereby the soot contained in the DPF is burned, that is to say oxidized. A DPF physical model returns a soot loading evaluation starting from the differential pressure signal read across the DPF by an exhaust gas pressure sensor (EGP) 283, as shown in Fig. 3. This basic information is corrected, considering the pressure drop on the DPF filter in clean conditions and divided by the volumetric flow rate of the exhaust gas, in order to obtain the so called "flow reSistance" of the exhaust gas. The physical model soot estimation is directly correlated to the flow resistance information, that is returned to give an accurate indication of the soot storage level at different exhaust conditions (especially in terms of temperature), wherein the only EGP information could not be accurate. This physical model is applied to the overall soot estimation strategy in order to optimize the DPF regeneration frequency.

As mentioned, a particular condition related to the estimation of the soot loading is the so called DPF parking effect: such effect takes place during a prolonged engine shut-off (an engine shut-off time threshold can be established by calibration) and if the soot in DPF reaches very low temperatures (such temperature can be evaluated at the DPF inlet and a DRE inlet temperature threshold can be established by calibration). During this period, the particulate properties change leading to a remarkable decrease of the pressure signal evaluated across the DPF. It means that, when the vehicle is started again, even if the soot quantity is not changed in the trap, there is a significant pressure drop reduction across the filter, thus leading to a soot loading under-estimation by the DPF physical model, which takes into account the information of the EGP 283.

In Fig. 4 is schematically shown the so called parking effect. In the upper part, (Fig. 4a) the soot layer 500 is deposited on the DPF substrate 510. This is a normal situation before the vehicle parking, for example in a case when there is a high pressure drop, due to the fact that the last regeneration process was performed a long time ago; in the middle part (Fig.4b), supposing a long parking time and low DPF inlet temperature, the increasing of the soot permeability leads to a lower pressure drop sensed across the filter, notwithstanding the soot layer 501 remains unchanged; finally, Fig. 4c shows that after the parking the following soot 500 has the same properties of the soot before the vehicle parking: therefore, the pressure drop will increase accordingly to the soot loading, but starting from a lower level and without any possibility to consider this offset until a regeneration process will take place.

As already mentioned, during the vehicle parking the soot quantity stored inside the DPF does not change. But, in terms of physical model behavior, this results in a lower drop of the differential pressure measured across the filter. This is due to the fact that the particulate permeability increases during this period, so that the particulate offers a lower resistance and consequently there is a lower pressure drop across the DPF. The pressure drop reduction leads to an under-estimation of the physical soot model based on it. In Fig. 5, the DPF loading and the coolant temperature behavior vs. time are shown. As can be seen, after the cold start, the apparent DPF loading is lowered by about 28%. As soon as new soot is stored inside the DPF. the physical model of the DPF loading will show an increase of the loading itself, but without taking into account the lost amount prediction.

This potentially drastic under-evaluation by the soot physical model might lead to a not properly working DPF regeneration, due to a real amount of soot too high at the beginning of the regeneration process.

According to an embodiment of the present invention, the computer program implements a strategy, which is able to learn the EGP pressure drop reduction, due to the parking effect, once a parking effect has been detected. This offset is added to the DPF physical model in order to have a reliable estimation. In other words the aim is to determine a learning logic that compensates the pressure drop reduction through a comparison between a reference flow resistance (index of permeability) and the current flow resistance of the exhaust gas, since the flow resistance is strictly correlated to the soot loading value.

The main steps of the computer program implementing the present strategy are the followings. Once the parking effect is recognized, a certain amount of time is necessary In order that the pressure drop provided by the EGP sensor starts decreasing. During this period the DPF physical model is considered not reliable regardless parking effect, and so it is usually frozen. Therefore, the engine should be operating for a time interval larger than a first predetermined time threshold tDEL to enable the strategy. This threshold can be about s. Then, once the EGP sensor signal becomes stable, the flow resistance value needs to be compared with the theoretical value it would have been had if no parking effect had taken place. A logical operation able to catch the flow resistance theoretical value, to compare it to the actual one and to get an offset to be added is required. Finally, after a certain time, during which the offset learning is enabled, the flow resistance starts increasing due to the new soot stored in the DPF. The learning phase shall end, and the calculated offset has to be added to the flow resistance signal till the start of the following DPF regeneration. Therefore, a second predetermined time threshold Max t2 for the offset learning time is requested to disable the strategy. This second threshold can be in a range of about 2 mm.

A better understanding can be obtained with reference to Fig. 6, which is a graph depicting the behavior of the soot loading physical model due to the parking effect. During the first phase a), limited by the first time threshold tDEL, the DPF physical model is considered not reliable regardless of parking effect recognition, and therefore it is frozen. The EGP offset learning strategy is off. During the second phase b), as soon as the DPF physical model starts working, the EGP sensor signal decreases because of parking effect. In this phase the offset learning strategy is enabled in order to prevent the pressure drop and the consequent soot loading under-estimation. Finally, during the third phase c) the flow resistance increases because of the new soot stored in the DPF. The offset evaluation strategy is disabled, and the offset got at the end of previous phase will be added to the soot loading amount coming from the EGP physical model, until the next DPF regeneration is called.

Therefore, with reference to Fig. 7, representing a high level flowchart of the computer program, the strategy will perform the following steps. At first, the parking effect recognition PER will be determined S710: after the engine disuse due to the vehicle parking, with the engine in the condition wait for cranking", the DPF parking effect is identified if the engine shut-off time is larger than a time threshold tOFF (for example, Oh) and the DPF inlet temperature is smaller than a temperature threshold Ti (e.g. -20 °C). Then, the learning phase start PELPS is enabled S720 if the engine is operating for more than a second time threshold tDEL.

Then, the learning phase PEL is performed S730. With reference to Fig. 8, the learning phase is based on: the activation of two timers ti, t2, whose function will be explained later; the calculation of the theoretical or reference flow resistance RFR, starting from the soot value S at the end of the previous driving phase, which comes from the DPF physical model. In other words, the reference flow resistance is calculated as a function of the previous soot level PSL estimated by the physical model. Preferably, the integrated soot rate ISR coming from a statistical model is added to the previous soot level, to account the soot loaded during the learning phase. Therefore, the reference flow resistance is the flow resistance corresponding to the sum of the previous soot level PSL, coming from the DPF physical model and available at the previous engine shut-off, and the new soot that is instantaneously stored after the engine restart, evaluated according to the statistical model; Then, the reference flow resistance RFR is compared to the current flow resistance CFR and the offset, better, the correction factor FRcorr of the exhaust gas flow resistance is determined. In other words the flow resistance correction is the arithmetic average of the difference between the reference flow resistance and the current flow resistance, all along the learning period elapsed. The flow resistance correction FRcorr is added to the current flow resistance CFR and once the learning period is ended, the last calculated FRcorr value is kept to correctly estimate the soot loading value, till the following regeneration will be needed.

S

Finally, the learning phase end PELPE occurs S740 when the first of the two timers overcome its corresponding time threshold. In fad, with reference to Fig. 9, the duration of the learning phase is based on a first timer t2 that starts t2otM when the DPF inlet temperature I DPF in is larger than a temperature threshold T PE End, over which experimental data point out that the parking effect ends. The learning phase end PELPE will occur when the first timer t2 will be larger than the time threshold Max t2.

In parallel, a second timer ti, which starts t1 at the same time of the learning phase start PELPS, has to limit the total duration of the learning phase in any condition, if this second timer is larger than a time threshold Max ti, which can be in the range of about 5 mm. The time step of these two timers is a weighted time step TSW, as a function of the flow resistance reliability FRrel (based on pressure drop and volumetric flow signals), to increase the learning phase in case, when most of the learning time has been spent in low accurate regions.

Summarizing, the strategy implemented by the present computer program allows to obtain many beneficial effects: an improved accuracy in soot loading evaluation results; an optimization of regeneration frequency, so avoiding risks of thermal stress and/or crack for the component, due to the soot under-estimation; a reduced calibration effort, due to the lower importance of back-up models, as the statistical models.

While at least one exemplary embodiment has been presented in the foregoing summary and detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or exemplary embodiments are only examples, and are not intended to limit the scope, applicability, or configuration in any way. Rather, the foregoing summary and detailed description will provide those skilled in the art with a convenient road map for implementing at least one exemplary embodiment, it being understood that various changes may be made in the function and arrangement of elements described in an exemplary embodiment without departing from the scope as set forth in the appended clams and their legal equivalents.

REFERENCE NUMBERS

data carrier automotive system 110 internal combustion engine engine block cylinder cylinder head camshaft 140 piston crankshaft combustion chamber cam phaser fuel injector 165 fuel injection system fuel rail fuel pump fuel source intake manifold 205 air intake duct 210 intake port 215 valves 220 port 225 exhaust manifold 230 turbocharger 240 compressor 245 turbocharger shaft 250 turbine 260 intercooler 270 exhaust system 275 exhaust pipe 280 aftertreatment devices 262 Diesel particulate filter 283 exhaust gas pressure sensor 290 VGT 300 exhaust gas recirculation system 310 EGR cooler 320 EGR valve 330 throttle body 340 mass airflow, pressure, temperature and humidity sensor 350 manifold pressure and temperature sensor 360 combustion pressure sensor 380 coolant temperature and Level sensors 385 lubricating oil temperature and level sensor 390 metal temperature sensor 400 fuel rail digital pressure sensor 410 cam position sensor 420 crank position sensor 430 exhaust pressure and temperature sensors 440 EGR temperature sensor 445 accelerator position sensor 446 accelerator pedal 450 ECU 500 soot layer (high pressure drop) 501 soot layer (after parking effect, low pressure drop) 510 DPF substrate S710 step S720 step S730 step S740 step PER parking effect recognition PELPS parking effect learning phase start PEL parking effect learning PELPE parking effect learning phase end tDEL time threshold Max t2 time threshold tUrF time threshold t2 first timer t25 first timer start ti second timer t1 second timer start Max ti time threshold TSW weighted time step Ti temperature threshold T DPF in DPF inlet temperature T RE End temperature threshold RFR reference flow resistance CFR current flow resistance FRcorr flow resistance correction FR rel flow resistance reliability S soot value PSL previous soot level, by the physical model ISR integrated soot rate, by the statistical model

Claims (8)

1. CLAIMS1. A computer program comprising a computer code suitable for performing a method of controlling a Diesel particulate fitter (OFF, 282) of an internal combustion engine (110), wherein the method controls a DPF soot loading phase after an engine shut-off, by carrying out the following steps: -before engine cranking, determining if an engine shut-off time is larger than a first time threshold (tOFF) and a DPF inlet temperature is smaller than a first temperature threshold (TI); -determining a learning phase start (PELPS) if the engine is operating for more than a second time threshold (tDEtj: -performing a learning phase (PEL) consisting in: o activating two timers (ti, t2), o calculating a correction factor (FRcorr) of an exhaust gas flow resistance as the average difference, during said learning phase, between a reference flow resistance (RFR), which is a function of a calculated soot loading value (8), and a current flow resistance (CFR), and o correcting the current flow resistance with this correction factor (FRcorr); -determining a learning phase end (PELPE) if at least one of the two timers (ti, t2) is larger than a corresponding time threshold (Max ti, Max t2).
2. 2. Computer program according to claim 1, wherein said soot loading value (8) is a previous soot level (PSL) estimated by a soot loading physical model.
3. 3. Computer program according to claim 2, wherein said soot loading value (S) is calculated as the sum of the previous soot level (PSL), estimated by the soot loading physical model, and an integrated soot rate (ISR), estimated by a soot loading statistical model.
4. 4. Computer program according to any of the preceding claims, wherein the first timer (t2) starts if a DPF inlet temperature (1 DPF in) is larger than a second temperature threshold (T FE End) and ends if it is larger than a time threshold (Max t2) of about two minutes.
5. 5. Computer program according to any oil the preceding claims, wherein the second timer (ti) starts at the same time of the learning phase start (PELPS) and ends if it is larger than a time threshold (Max tl) of about five minutes.
6. 6. Computer program according to any of the preceding claims, wherein a weighted time step (TSW) is established for each of these two timers (ti, t2) and is a function of a flow resistance reliability (FR rel).
7. 7. Automotive system (100) comprising an internal combustion engine (110) and an electronic control unit (460), the engine comprising an aftertreatment system having at least a particulate filter (282), wherein the electronic control unit is configured to perform the computer program according to any of the preceding claims.
8. 8. Computer program product on which the computer program according to claim 1-6 is stored.