FR3063769A1 - METHOD FOR MONITORING THE OPERATING STATE OF A PARTICLE FILTER AND EXHAUST GAS POST-PROCESSING SYSTEM - Google Patents

Abstract

The method of monitoring the operating state of the particulate filter is implemented in an aftertreatment system of the exhaust gases of a heat engine, the method using a measurement of a differential pressure (dP ) between an input and a filter output. According to the invention, the method comprises: 1 °) a continuous learning of a filtration efficiency ratio (RT) of the filter to establish a reference ratio (RTA), the filtration efficiency ratio being calculated between the measured differential pressure (dP, dPn) and an integrated differential pressure (dPI, dPIn) provided by a model (S30) of the filter; 2 °) a comparison (S7, S70) of a current value of the ratio of filtration efficiency to the reference ratio; and 3 °) a detection (S8) of a crack in the filter according to the result of the comparison.

Description

Holder (s): PEUGEOT CITROEN AUTOMOBILES SA Société anonyme.

Extension request (s)

Agent (s): PEUGEOT CITROEN AUTOMOBILES SA Public limited company.

154J METHOD FOR MONITORING THE OPERATING STATE OF A PARTICLE FILTER AND EXHAUST GAS AFTER-TREATMENT SYSTEM.

FR 3 063 769 - A1 _ The method for monitoring the operating state of the particulate filter is implemented in an exhaust gas aftertreatment system of a heat engine, the method calling for a measurement differential pressure (dP) between an inlet and an outlet of the filter. According to the invention, the method comprises: 1) continuous learning of a filtration efficiency ratio (RT) of the filter to establish a reference ratio (RT _a ), the filtration efficiency ratio being calculated between the measured differential pressure (dP, dP _n ) and an integral differential pressure (dPI, dPl _n ) supplied by a model (S30) of the filter; 2) a comparison (S7, S70) of a current value of the filtration efficiency ratio to the reference ratio; and 3 °) a detection (S8) of a crack in the filter as a function of the result of the comparison.

METHOD FOR MONITORING THE OPERATING STATE OF A PARTICLE FILTER AND EXHAUST GAS AFTER-TREATMENT SYSTEM [001] The invention relates generally to the depollution of exhaust gases emitted by the heat engine of a vehicle .

More particularly, the invention relates to a method for monitoring the operating state of a particle filter. The invention also relates to an exhaust gas after-treatment system including a particle filter and in which the above-mentioned process is implemented. Finally, the invention relates to a vehicle equipped with such an exhaust gas aftertreatment system and to a computer program.

In motor vehicles, exhaust gas aftertreatment systems have today become essential for compliance with environmental standards. Regulated pollutant emissions include carbon monoxide (CO), unburnt hydrocarbons (HC), nitrogen oxides (NOx) and soot particles.

For the removal of soot particles in exhaust gases, it is well known, in particular on diesel type engines, to equip exhaust gas aftertreatment systems with a particle filter. With the next entry into force of the environmental standard known as "Euro 6.3", particle filters are also becoming more common in petrol-type engines.

Particulate filters are effective in reducing particulate pollution. However, the gradual accumulation of soot particles retained in the porous layers of the filter causes a pressure drop in the exhaust line of the heat engine. Periodic regeneration at high temperature is necessary to remove the accumulated soot particles by combustion.

During the life of the vehicle, the particulate filter degrades due to the severe thermal conditions to which it is subjected during regeneration. Cracking of the filter can occur during a very severe regeneration phase and make the filter faulty with regard to the emission of particles, for example, in the case of a clogged filter where the combustion of soot would produce in excess of oxygen and with a low gas flow. Cracking of the filter can also occur suddenly due to malicious manipulation.

The cracking of the filter leads to particle emissions which exceed the maximum threshold set by the regulations. This “cracking” event must be detected by the on-board diagnostic system, called OBD for “On Board Diagnostic” in English. It must also be signaled to the user of the vehicle by the activation of a warning light on the dashboard, namely, the MIL warning light for "Malfunction Indicator Lamp" in English, to inform him that a repair of the vehicle is required.

By FR2976620 and FR2963640, it is known to detect a cracking of the particulate filter from a differential pressure, representative of a pressure drop, this differential pressure being supplied by a sensor which measures the absolute pressure upstream and downstream of the filter.

From the differential pressure between the inlet and the outlet of the filter, it is possible to obtain an evaluation of the loss of filtration efficiency. However, in the state of the art, the detection of the loss of efficiency from the differential pressure lacks precision. In addition, the amount of soot in the filter represents a disturbing element in the diagnosis of cracking of the filter, as well as the dispersion specific to the differential pressure sensor, the manufacturing dispersion of the filter ceramic and the dispersion of the measurement. the exhaust gas flow. The addition of a soot sensor to measure the amount of soot would allow a better diagnosis, but such a sensor has the disadvantage of an increase in cost which is all the more penalizing that this sensor cannot be used for other control strategies.

There is therefore a need for a method for monitoring the operating state of a particulate filter which allows reliable detection of the presence of a crack therein.

According to a first aspect, the invention relates to a method for monitoring the operating state of a particulate filter in an exhaust gas aftertreatment system of a heat engine, the method using to a measurement of a differential pressure between an inlet and an outlet of the particulate filter. According to the invention, the method comprises:

continuous learning of a filtration efficiency ratio of the particulate filter to establish a reference ratio, the filtration efficiency ratio being calculated between the measured differential pressure and an integral differential pressure provided by a model of the particles;

a comparison of a current value of the filtration efficiency ratio with the reference ratio; and detecting a crack in the particulate filter based on the result of the comparison.

According to a particular characteristic, the differential pressure measurement occurs when predetermined prerequisites are satisfied.

According to another particular characteristic, the predetermined prerequisites include at least any of the following conditions:

- an estimated volume flow of the exhaust gases upstream of the particulate filter within a predetermined value range;

- an operating point of the stabilized or quasi-stabilized particle filter;

- a mass of soot estimated in the particle filter zero or less than a predetermined threshold;

- an external pressure included in a predetermined value interval;

- an outside temperature within a predetermined value range;

- a minimum engine speed and a predetermined combustion mode;

- a total distance traveled since a last regeneration of the particle filter included in a predetermined value interval; and

- a last regeneration not overloaded, with a mass of soot before the initiation of regeneration below a predetermined threshold.

According to yet another particular characteristic, the filtration efficiency ratio is calculated between a measured average of several samples of differential pressure measured and an integral average of several samples of differential pressure integrates.

According to yet another particular characteristic, the average measured is obtained from at least two successive samples of measured differential pressure and the integral average is obtained from at least two corresponding samples of integral differential pressure.

According to yet another particular characteristic, the learning is carried out on one or more successive occurrences of the predetermined prerequisites.

According to another aspect, the invention also relates to a system for post-treating the exhaust gases of a vehicle heat engine comprising a particle filter equipped with a differential pressure sensor and a control unit, and comprising a software module installed in the control unit for the implementation of the method described briefly above.

According to other aspects, the invention also relates to a vehicle equipped with the system described briefly above for post-treatment of the exhaust gases emitted by the engine of the vehicle, and a computer program comprising program code instructions for executing the steps of the method briefly described above, when the program is executed by a processor.

Other advantages and characteristics of the present invention will appear more clearly on reading the detailed description below of several particular embodiments of the invention, with reference to the accompanying drawings, in which:

Fig. 1 is an overview of a particular embodiment of an exhaust gas post-treatment system according to the invention;

Fig.2 is an algorithm showing steps of the method according to the invention for monitoring the operating state of a particulate filter; and Figs. 3 and 4 are curves showing changes over time in a filtration efficiency ratio used in the process according to the invention.

A particular embodiment 1 of an exhaust gas post-treatment system in which the method according to the invention is implemented for monitoring the operating state of the particle filter is now described with reference to Fig. 1.

The exhaust gas post-treatment system 1 described here is intended for a diesel type engine. However, it should be noted that the invention is not limited to an application to diesel type heat engines and is also applicable to petrol heat engines.

As shown in Fig.1, the exhaust gas post-treatment system 1 essentially comprises an oxidation catalyst 2, a compact device 3 for post-treatment of the exhaust gases EG and an electronic control unit 4.

The compact exhaust gas post-treatment device 3 here comprises an SCR catalyst 30 and a particulate filter 31. The SCR catalyst 30 and the particulate filter 31 are shown in Fig.1 as two separate components . Note however that the compact device 3 may advantageously be of the so-called “Compact Liquid System” type and integrate the catalytic layer SCR, called “washcoat”, in an impregnated form in the particle filter.

The particle filter 31 is equipped with a differential pressure sensor 310 which measures a differential pressure dP between the inlet and the outlet of the filter. Various other sensors and components 32 equip the compact device 3 and will not be detailed here.

The differential pressure dP, like other sensor measurement signals, is supplied to an input / output port 40 of the electronic control unit 4. The differential pressure dP is used by the control of the system 1 , in particular by the regeneration control of the particulate filter, and by the monitoring process of the particulate filter.

In this particular embodiment, the electronic control unit 4 is the vehicle engine control unit. As indicated above, the unit 4 has an input / output port 40 through which the various signals necessary for controlling the functional components of the exhaust gas aftertreatment system 1 pass. A communication port 41 is also provided in unit 4 and authorizes a connection of the latter to a CAN type communication network of the vehicle. A dedicated software module is installed in unit 4 for the implementation of the particulate filter monitoring method according to the invention, by the execution of program code instructions by a processor of unit 4.

An embodiment of the particulate filter monitoring method according to the invention is now described in detail using the algorithm shown in Fig.2 and the curves of Figs.3 and 4.

As shown in Fig.2, the monitoring process has eight main steps S1 to S8.

Steps S1 to S4 essentially relate to the acquisition of differential pressure measurements for the calculation of a filtration efficiency ratio. Steps S5 to S8 mainly concern the continuous learning of a reference ratio and the detection of cracking of the filter.

In step S1, it is expected that optimal preconditions CPO are satisfied for a measurement of the differential pressure dP intended for the calculation of the ratio RT.

These optimal CPO prerequisites may include in particular one, several or all of the following conditions:

- an estimated volume flow Qvol of the exhaust gases EG upstream of the particulate filter which must be included in a predetermined value interval [Qvol _min , QvOl _max ],

- an operating point PF of the filter which is stabilized or almost stabilized;

- an estimated soot mass MSE in the filter which is zero or less than a predetermined threshold;

- an external pressure PA which must be included in a predetermined value interval [PA _min , PA _max ];

- an outside temperature TA which must be included in a predetermined value interval [TA _min , TA _max ];

- a minimum engine speed EM and a predetermined combustion mode MC;

- a total distance DT traveled since the last regeneration of the filter which must be included in a predetermined value interval [DT _min , DT _max ]; and

- a last RGD regeneration not overloaded, that is to say, a regeneration which took place under normal conditions, with a mass of soot before the initiation of the regeneration which was below a predetermined threshold.

It will be noted that the information necessary for implementing the method according to the invention is available in the electronic control unit 4, or else is accessible through the CAN network to which the unit 4 is connected. , the unit 4 hosts the control strategy for the entire post-processing system 1 and has the information used by the processing chain, in particular the differential pressure dP of the filter, the pressure PA and the outside temperature TA, the engine speed EM, etc., as well as those used by the supervisor of the particle filter, namely, the estimated volume flow Qvol, the estimated soot mass MSE, the distance DT and the time since the last regeneration. The combustion engine MC mode is informed by the combustion modes coordinator.

The conditional step S10 is provided for detecting the satisfaction of the optimal preconditions CPO. The flag F _CP0 = 1 indicates that the preconditions CPO are satisfied. As long as the flag F _CP0 # 1, the output N is active and the monitoring process loops pending on steps S1, S10. When F _CP0 = 1, the output Y of the conditional step S10 becomes active and the process proceeds to steps S2 and S3.

[0034] The steps S2 and S3 are steps of acquisition of samples, and dP _n DPL _n, which are executed in parallel and associated with steps S21 and S31 of averaging, dP _moy and DPL _m oy, respectively .

In step S2, each time an occurrence F _CP0 = 1 _occurs , step S10, the value of the differential pressure dP of the filter is assigned to the sample dP _n . The process loops from step S2 to steps S1, S10 until N samples dPi, ... dP _n , ... dP _N have been taken. The conditional step S20 interrupts the looping when the number of samples N is reached. The samples dPi to dP _N are summed and the sum obtained ZdP _n is divided by N, in S21, to obtain an average of differential pressure measured dP _avg .

In step S3, each time an occurrence F _CP0 = 1 _occurs , step S10, a value of a differential pressure integrates dPI supplied by an integral filter model, S30, is assigned to the sample. dPl _n .

The integral pressure differential dPI is that which would be obtained with a new filter, or integral, having a filtration efficiency of 100%. As shown in S30, the pressure dPI is given by an integral filter model as a function of the volume flow rate Qvol of the exhaust gases. The integrated filter model can be produced with a map or by calculation.

The process loops from step S3 to steps S1, S10 until N samples dPh, ... dPl _n , ... dPI _N have been taken. The conditional step S20 interrupts the looping when the number of samples N is reached. Dph samples dPI _N are summed and the sum obtained is divided by N ZdPIn, in S31, to obtain a differential pressure medium incorporates DPL _Avg.

The number of samples N for calculating the means of differential pressures dPmoy and dPImoy will be at least equal to 2. In general, the number of samples N will be calibrated according to the applications, so as to obtain the desired robustness .

[0040] In step S4, RT filtration efficiency ratio is calculated, RT = _(average dP / DPL _avg).

Steps S5 and S6 correspond to the continuous learning of a RT filtration efficiency reference ratio _A from the RT ratios calculated successively.

Step S5 limits the RT ratio in high and low values.

In this embodiment, step S6 applies an integration by averaging over M successive values of the ratio RT calculated in step S4, so as to obtain the reference ratio, or learned ratio, RT _A having sufficient robustness. Learning the ratio RT _A therefore requires a number of occurrences F _CP0 = 1 which is equal to MxN. The number M will be calibrated according to the applications or dynamically by the process. Still in step S6, at the end of learning, the learned ratio RT _A is stored in a non-volatile memory NVM.

In step S7, a difference in ratio Δ _ρτ = (RT _A - RT) is calculated between the current ratio RT, calculated in step S4, and the learned ratio RT _A stored in the memory NVM.

In the conditional step S70, it is detected whether the difference in ratio Δ _ρτ exceeds or not a predetermined threshold TH. If the difference in ratio Δ _ρτ is greater than the TH threshold (output

Y), the monitoring process detects a crack in the particulate filter and a FAP _NO k fault code is issued in step S8. Otherwise, the monitoring process loops to steps S1, S10, waiting for a next occurrence

F cpo = 1 The curves in FIG. 3 show the changes over time in the differential pressure dP and in the filtration efficiency ratio RT. During the first period P1, the curves noted correspond to running with an integral particle filter, that is to say, a filter having a filtration efficiency of 100%. During the second period P2, the particulate filter is severely degraded suddenly and only retains a filtration efficiency of 55% in this example. At time t1, corresponding to the sudden degradation of the particle filter, the curve RT presents a negative variation D _RT of significant value. This significant variation D _RT is greater than the detection threshold TH (Fig. 2, S70) and authorizes the detection of a cracking of the particulate filter.

The filtration efficiency ratio curves RT1 and RT2, shown in FIG. 4, are obtained by simulation from first and second differential pressure measurements dP1 and dP2. The curves RT1 and RT2 show the robustness of the detection obtained with the method of the invention with respect to the dispersion of the differential pressure dP.

The differential pressure dP1 is here the nominal measurement. The differential pressure dP2 is here a measure derived from dP1 to which has been added a high overall dispersion -AdP greater than 3 standard deviations, dP2 = dP1-AdP. Such a dispersion greater than 3 standard deviations has a very low probability of occurring. The overall dispersion -AdP represents the sum of the dispersions due to the measurement accuracy of the differential pressure sensor, the disparity in the manufacture of the filter and the accuracy in the evaluation of the gas flow rate.

At time t2, corresponding to the application of the dispersion -AdP, the curve RT2 undergoes a negative variation D0 _RT . At time t3, corresponding to the sudden degradation of the particle filter, as well as for the curve RT of FIG. 3, the curve RT2 presents a negative variation D2 _RT of significant value, analogous to that D1 _RT of the curve RT1. As it appears in Fig.4, the variation D0 _RT introduced by the application of the dispersion -AdP remains much lower than the variations D2 _RT , D1 _RT which correspond to the cracking event of the filter to be detected. The cracking event can therefore be easily discriminated with the TH threshold. The method according to the invention allows robust detection of a crack in the particulate filter, detection which is not affected by the dispersion.

The invention is not limited to the particular embodiments which have been described here by way of example. Those skilled in the art, depending on the applications of the invention, may make various modifications and variants which fall within the scope of the claims appended hereto.

Claims (10)

1. A method for monitoring the operating state of a particulate filter (31) in an exhaust gas (EG) aftertreatment system (1) of a heat engine, said method using a measurement of a differential pressure between an inlet and an outlet of said particulate filter (31), characterized in that it comprises: continuous learning of a filtration efficiency ratio (RT) of said particle filter (31) to establish a reference ratio (RT _A ), said filtration efficiency ratio (RT) being calculated between said differential pressure measured (dP, dP _n ) and an integral differential pressure (dPI, dPl _n ) supplied by a model (S30) of the particle filter (31); a comparison (S7, S70) of a current value of said filtration efficiency ratio (RT) to said reference ratio (RT _A ); and detecting (S8) a crack in said particle filter (31) based on the result of said comparison (S7, S70). 2. Method according to claim 1, characterized in that said differential pressure measurement (dP, dP _n ) occurs when predetermined prerequisites (CPO) are satisfied. 3. Method according to claim 2, characterized in that said predetermined preconditions (CPO) include at least any of the following conditions: - an estimated volume flow (Qvol) of the exhaust gases (EG) upstream of said particulate filter (31) included in a predetermined value interval (Qvolmin, Qvol max) i - an operating point (PF) of said stabilized or quasi-stabilized particle filter (31); - an estimated soot mass (MSE) in said particle filter (31) zero or less than a predetermined threshold; - an external pressure (PA) included in a predetermined value interval (PA _min , PA _max ); - an outside temperature (TA) included in a predetermined value interval (TA _min , TA _max ); - a minimum engine speed (EM) and a predetermined combustion mode (MC); a total distance (DT) traveled since a last regeneration (RGD) of said particle filter included in a predetermined value interval (DT _min , DT _max ), and - a last regeneration (RGD) not overloaded, with a mass of soot before the initiation of regeneration below a predetermined threshold. 4. Method according to any one of claims 1 to 3, characterized in that said filtration efficiency ratio (RT) is calculated between a measured average (dP _avg ) of several samples of measured differential pressure (dP _n ) and an average integrates (DPL _mean) of several differential pressure samples includes (DPL _n). 5. Method according to any one of claims 1 to 4, characterized in that said measured average (dP _avg ) is obtained from at least two said successive samples of measured differential pressure (dP _n ) and said average integrates ( DPL _avg) is obtained from at least two said corresponding samples integrates differential pressure (DPL _n). 6. Method according to any one of claims 1 to 5 and claim 2, characterized in that said learning is carried out on one or more successive occurrences of said predetermined prerequisites. 7. Post-treatment system (1) for the exhaust gases (EG) of a vehicle heat engine comprising a particle filter (31) equipped with a differential pressure sensor (310) and a control unit ( 4), characterized in that it comprises a software module installed in said control unit (4) 5 for the implementation of the method according to any one of claims 1 to 6. 8. System according to claim 7, characterized in that said control unit (4) is the engine control unit of said vehicle. 9. Vehicle comprising a heat engine, characterized in that it comprises a 10 system (1) according to claim 7 or 8 for an aftertreatment of the exhaust gases emitted by said heat engine. 10. Computer program comprising program code instructions for the execution of the steps of the method according to any one of claims 1 to 6, when said program is executed by a processor (4).