WO2013079839A1

WO2013079839A1 - Method and device enabling the continuous estimation of the cylinder compression ratio of an engine

Info

Publication number: WO2013079839A1
Application number: PCT/FR2012/052475
Authority: WO
Inventors: Annabelle Cornette; Olivier Hayat; Alexandre MAZER
Original assignee: Peugeot Citroen Automobiles Sa
Priority date: 2011-11-28
Filing date: 2012-10-26
Publication date: 2013-06-06
Also published as: FR2983244B1; FR2983244A1; EP2786004B1; EP2786004A1

Abstract

The invention relates to a method enabling continuous estimation of the cylinder compression ratio of an internal combustion engine, said engine including a plurality of cylinders, at least one compression ratio measurement probe, a plurality of injectors connected to the cylinders, and a flow meter. The method according to the invention includes the following steps: calculating the simple compression ratio model (Φ_Calc) by means of the set fuel quantity (qlnj) to be injected into a cylinder and from the measurement of the flow rate of intake air (Qair); identifying and quantifying the possible variances and derivations of the compression ratio at stabilized and/or temporary operating points by means of at least one derivation identifier (3); and continuously correcting the calculated compression ratio (Φ_Calc) by using the previously determined derivation values.

Description

METHOD AND APPARATUS FOR CONTINUOUS ESTIMATING OF THE CYLINDER WEIGHT OF AN ENGINE

The present invention relates to a method and a device for continuously estimating the cylinder richness of an engine. It concerns in particular, but not exclusively, a method and a device for continuously estimating the cylinder richness of a diesel engine, by means of a measurement of richness achieved by a sensor such as a probe.

It is known that the values relating to the richness of an engine can be measured by means of a probe, such as a lambda or NOx probe, which makes it possible to obtain precise values in stabilized speeds.

However, it turns out that the implementation distance of the probe relative to the cylinder, the elements of the exhaust line arranged upstream of the probe, or the operating principles of the probe cause a delay and a filtering of this measurement which are likely to vary according to the stabilized operating points. As a result, the values relating to the richness thus measured are not perfectly representative of the real wealth specific to a cylinder during dynamic operation.

It is also known that information relating to the richness of an engine can also be estimated by performing a calculation using the parameters of the admission air flow rate (measured or estimated) and the injected fuel flow rate ( valued). The result obtained has the advantage of having been calculated taking into account the dynamics associated with said parameters, as well as the slowness of establishing the air loop relative to that of the fuel circuit. However, the determination of the richness of an engine by proceeding in this way does not eliminate the dispersions and drifts of the engine components, such as injectors, the fuel pump, the flow meter measuring the air admitted. A dynamic but potentially derivative signal is thus obtained.

The estimation of the richness of an engine can also be achieved by mapping this estimate according to parameters characterizing the operating point which can be typically the speed and torque.

However, a wealth engine thus mapped does not present the good representative dynamic of the real wealth of the cylinder (due in particular to a too low consideration of the slow establishment of the air loop compared to that of the fuel system), nor sufficient robustness vis-à-vis the dispersions generated by the engine components.

Also known is EP 0 643 21 1 which relates to a device for estimating the air / fuel ratio of a mixture supplied to an internal combustion engine, by means of a probe which can be of type 02. The implementation of this invention results in the modeling of the behavior of the probe and the use of a Kalman filter.

This device makes it possible to estimate the cylinder-by-cylinder richness, by detecting in the exhaust puffs the relative impact of the different combustions on the overall richness, the measurement of which is carried out by means of a probe disposed at the level of the collector. exhaust. The estimation of the richness per cylinder is weighted by previously identified coefficients in a table.

It turns out, however, that this device has the following drawbacks:

• it is implemented only on stabilized operating points; it can not be transient; • it does not make it possible to recalibrate the global richness according to the possible dispersions and drifts;

• the probe model used only operates on a stabilized operating point and is not adaptable from one motor to another due to dispersions;

• the use of NOx probes positioned very downstream in an exhaust line and having a wealth signal more filtered than that of a probe 02 placed further upstream does not seem suitable when using this device; Indeed, the peaks of wealth caused by the exhaust puffs and the instructions of "wobbling" are not visualizable with this type of probe.

[001 1] Finally, patent application WO 07 041 092 discloses a system and a method for controlling a diesel engine by means in particular of sensors shaped to detect at least one specific component of the exhaust gas, and that the patent application EP 1 413 728 which relates to a control unit and a control method of a NOx probe placed in the exhaust pipe of an internal combustion engine.

However, the devices subject of these patent applications do not make it possible to dispense with type 02 probes, and to allow both an estimate of the cylinder richness of an engine on stabilized and possibly transient points.

The present invention therefore more particularly aims to solve these problems by providing a method for continuously estimating the cylinder richness of an engine, with high accuracy and good dynamics, the implementation of the method can advantageously on the basis of stabilized operating points but also in transient. To this end, the invention relates to a method for continuously estimating the cylinder richness of an internal combustion engine, said engine comprising a plurality of cylinders, at least one wealth measurement probe, a plurality of injectors connected to the cylinders, a flow meter,

said method comprising the steps of:

• calculate the simple model of t = _Calc richness by means of the injection fuel quantity qlnj in a cylinder and the measurement of the air intake flow Qair;

· Identify and quantify any dispersions and potential drifts on stabilized and / or transient operating points using at least one drift identifier;

• continuously correct the calculated wealth t> _Calc using the values of the drifts determined previously.

In this way and advantageously, the device according to the invention allows a continuous estimation of the engine cylinder richness using a calculated model of wealth, and recaling the result of this calculation by relative to the dispersions and possible drifts in order to obtain a continuous correction of the calculated value of richness.

According to an alternative embodiment, the method according to the invention may also comprise an additional step making it possible to identify and quantify the possible transient dispersions and wealth drifts by means of at least one additional drift identifier. preferably consisting of a Kalman filter which can be included in a calculator. In this way, the correction of the calculated wealth t> _Calc can be advantageously completed in order to refine the final value of the richness of the cylinder. Advantageously, the combined use of these two drift identifiers makes it possible to take into account, without any limit, the non-uniformity of the wealth drift over the entire engine field, as well as its motor-driven dispersion. .

The invention also relates to a device for continuously estimating the cylinder richness of an internal combustion engine, characterized in that it comprises a drift identifier and a Kalman filter shaped to implement the method or methods according to the invention.

An embodiment of the invention will be described below, by way of non-limiting example, with reference to the accompanying drawings in which:

Figure 1 is a schematic representation of the main elements used in the implementation of an alternative embodiment of the method according to the invention, with a highlighting of existing relationships between them.

FIG. 2 is a diagrammatic representation, in the form of curves, of the various richness and wealth drift signals produced during the implementation of the method according to the invention.

In this example, as shown in FIG. 1, the simple model of wealth t> _Calc is calculated by a calculator 1 by means of the fuel setpoint injection qlnj in a cylinder and the measurement of the flow rate. Qair air intake in the same cylinder.

The fuel charge to be injected qlnj is equivalent to the estimated value of the injected fuel flow, the air intake flow Qair being modeled or measured using a flowmeter.

The constitutive signal of the wealth model is the quotient of the injected fuel flow qlnj (or Qcarburantjnjected) on the intake air flow Qair or (Qair_cylindre), multiplied by the stoichiometric ratio γ. The signal thus obtained has the particularity of being at the right momentum, but potentially derived.

Φ 0- 'injected fuel

the cylinder _ estimated - Ύ i · - air _ cylinder

Advantageously, the constitutive signal of the wealth model allows:

• to take into account satisfactorily the dynamics associated with the two quantities qlnj and Qair; and

To obtain a correct representation of the phenomenon constituted by the slowness of establishment of the air loop with respect to that of the fuel circuit.

Furthermore, using a richness probe 2 which may be of the NOx type placed downstream in the exhaust line, preferably downstream of the catalyst SCR (Selective Catalytic Reduction), a measured signal of wealth is defined. This signal makes it possible to produce in transitory information on the wealth that is delayed and attenuated; on the other hand, in stabilized, the information carried by the signal is considered to be precise.

The richness probe 2 may be of the NOx type but it may also, for example, be of the lambda type.

The constitutive signal of the richness model and the measured signal of richness are carried as input of a drift identifier 3 which identifies and quantifies the dispersions and possible drifts of wealth on stabilized operating points.

The drift identifier 3 thus allows the realization of a possible drift training resulting in the establishment of a mapping of these wealth drifts in the engine field. In this way, the drift identifier 3 proceeds to a registration of the calculated model of wealth with respect to possible dispersions and drifts using the measured signal of wealth which has the advantage of being accurate in stabilized regimes. .

More precisely, the value of the calculated richness carried by the constitutive signal of the richness model is corrected continuously by said drift learning carried out by the drift identifier 3, these drifts being preferentially recorded in a map and then read again. without discontinuity with interpolation.

However, the value of richness obtained at the output of the drift identifier 3 may be recaled only incompletely due for example to incomplete learning, or approximations inherent to the storage method.

Advantageously, in order to solve this problem, the correction of the calculated richness t> _Calc can be advantageously completed in order to refine the final value of said richness of the cylinder, in particular by means of a Kalman filter 4 which allows to obtain a complementary dynamic identification of the wealth drift.

The general equation of the Kalman filter is as follows, the equations to be considered are more particularly the equation (1) of the table "Update Time" and the equation (2) of the table "Mise en jour up to date Measure ".

with

^{% k} : the prior estimate of the state vector at time k;

^Xk : the posterior estimation of the state vector at time k;

^{Uk ~ 1} : the vector of deterministic variables; in this case, it is the calculated wealth in the combustion chamber.

Two signals are applied to the input of the Kalman filter:

• the model 02 richness signal of the probe; and

• the measured signal of richness 02, sensor by the probe 2.

Each of these signals is assigned a relative confidence by introducing process noises w and measurement v.

The first filter processing step consists in applying to the model wealth signal 02 the alteration that it would suffer in the event that it would be picked up by the probe 2 of the NOx type.

This alteration consists of:

A pure delay due to the transport of the exhaust gases from the cylinder to the probe 2, to which is added the pure delay of the probe 2 itself; and

• a filter ^(1st or ^2nd order, for example). This alteration is only virtual because the probe 2 does not analyze this model signal 02, but the real signal of richness that is different from the

previous by the drift of wealth ° ² to identify. ^f n [0041] The Kalman filter thus creates a signal filtered calculated richness' 2 ° or z (and delayed, but upstream of the equation of state). This creates the 'a priori' state vector of the Kalman structure (see the equation of the state vector below). The second step of processing the Kalman filter, which is performed a posteriori, consists in comparing this virtual signal with the signal actually picked up and analyzed by the richness probe 2. The difference between these two signals g

defines the instant wealth drift ° ² . However, the behavior (generally characterized by the delay and attenuation) of the probe 2 can not be perfectly known. It is therefore necessary to limit the trust in this drift identification to not make it too random in case of errors (even transient). This limitation of confidence is obtained by means of the Kalman gain, which forces a convergence time before considering the fully identified drift. The finally estimated wealth signal is thus the model signal 02 recalibrated by the measurement of the probe 2 with a time of integration, linked to the necessarily limited confidence in the model of the behavior of the wealth probe 2.

The equation of state, which presents in this case four dimensions (assuming that the behavior model used was in 2nd order), is as follows: with:

Q

° ² : the identified drift, ie the difference between the measured richness and the calculated richness signal passed by the model of behavior of the probe;

° 2 _: calculated wealth (model);

n ^f

^ 2: the filtered computed wealth (order 1) in order to take into account the behavior of the probe;

° ⁱ : the calculated computed wealth (order 2) to take into account the behavior of the probe;

The vector composed of these four variables constitutes the state vector. 0. | j _ar _C h _esse calculated (model) at time k-1; 0 | hchesse _was measured by the probe at time k; : the signal noise model 'calculated wealth'; ^Vk : the sound of the measured signal of richness by the probe.

According to an alternative embodiment of the invention, the establishment of this state vector could be achieved by using a probe behavior model having greater variability, or by not showing the said model. , the latter being then defined upstream.

[0048] In this example of proposed implementation, the Kalman gain resulting from these calibration compromises is unique and pre-calculated, for the sake of simplicity of implementation. However, according to an alternative embodiment of the invention, at each computation step, measurement noise and process, and the resulting optimal Kalman gain (infinite gain Kinf) can be updated.

According to another embodiment of the invention, the Kalman gain can be mapped by separate areas of the motor field.

Advantageously, learning drift on stabilized points makes it possible to accelerate the work of the Kalman filter, to make it more robust, and even to ensure that the result provided is correct.

The combination of the Kalman filter and the stabilized learning technique ensures the overall high adaptability. Indeed, the wealth drift, which depends first and foremost on component drifts such as the flowmeter and the injectors, is not expected to be uniform in the engine field, but variable from one point to another in transient. A single Kalman filter, which by definition takes a significant amount of time to embed a drift and converge there, would see its dynamic of identification and updating conflict with the dynamics of the operating point and its associated wealth drift. . The filter would thus keep - at least partially - a specific drift to the previous point whereas this one would already have radically changed on the following point. Failing to immediately integrate the new drift, that retained and applied would possibly be shifted, and could even distort the correction to aggravate the estimate rather than improve it.

The stabilized learning is freed from the model of behavior of the probe 2, insofar as it no longer intervenes on a stationary point (the two input signals being constant, there is no longer any attenuation and cylinder-probe delay is inconsistent). A cartography of the drift of richness can thus be established (relying for example on the points regime-torque), the latter being obviously dependent on the stabilized points actually encountered on the road. By carrying out an interpolated reading in this mapping, the skilled person is then able to pre-position the wealth drift of the current point, and to limit the remaining work of the Kalman filter, while minimizing the possible impact of drift history preservation.

There are more ways to combine this mapping and the Kalman filter, these may for example consist of:

^■ use the drift read in the evolutionary cartography to directly correct the model 0 ₂ richness input signal of the Kalman 4 filter;

^■ use the drift read for the 'a priori' state of the Kalman 4 filter, allowing permanent pre-positioning more or less successful

(depending on the fill level of the map), which is then corrected 'a posteriori' via the measured signal of wealth <¾ sensor by the probe 2 and the Kalman gain. As shown in FIG. 3, the resulting identification of the wealth drift is carried out by proceeding as follows:

the estimated final richness signal (bold dots) is calibrated on the dynamics of the model of wealth modeled (fine continuous line), but recaled with respect to its drift at least partially, on transient points as on stabilized points ( drift observed by the

Kalman (vertical arrows), between the probe signal (measure of richness, continuous line in bold) and the virtual signal in fine dotted black).

[0057] Advantageously, the combination of the use of the drift identifier 3 and the Kalman filter 4 makes it possible in particular:

• to carry out an adaptive identification of the drifts, and not a fixed table calibration valid only on an operating point and / or a single engine; • to make an estimation of the richness cylinder benefiting from the advantages of a measurement obtained by means of a probe, constituted by the franking of the drifts and dispersions, but also the advantages provided by the simplicity of the calculation with perform that allow a dynamic registration of wealth;

• to link the model of emissions of particles to the final signal obtained, by ensuring a great robustness in transient as in stabilized; this therefore makes it possible to improve the performance and the robustness of the estimation of the emissions of particles and that of the associated loading of the particulate filter;

• to be able to adapt the strategy of dynamic registration of wealth to all sorts of exhaust systems;

• to dispense with Diesel operation, the use of a specific Lambda probe; the NOx probe whose presence is statutorily necessary downstream of the exhaust line makes it possible to obtain the desired wealth information, which makes it possible to save money.

Claims

1. A method for continuously estimating the cylinder richness of an internal combustion engine, said engine comprising a plurality of cylinders, at least one richness measuring probe, a plurality of injectors connected to the cylinders, a flowmeter,

characterized in that said method comprises the steps of:

• calculate a simple model of richness (t> _Calc) by means of the injection fuel quantity (qlnj) in a cylinder and the measurement of the intake air flow rate (Qair);

• identify and quantify dispersions and potential drifts on stabilized and / or transient operating points using at least one drift identifier (3);

• continuously correct the calculated wealth (t> _Calc) by using the values of the drifts determined previously.

2. Process for continuously estimating the cylinder richness of an internal combustion engine according to claim 1,

characterized in that it comprises a further step for identifying and quantifying potential transient dispersions and richness drifts using at least one additional drift identifier consisting of a Kalman filter (4).

3. Method according to claim 2,

characterized in that the constitutive signal of the richness model (t> _Calc) and a measured signal of richness are input to the drift identifier (3); the constitutive signal of the richness model (t> _Calc) corresponds to the quotient of the fuel flow injected (qlnj) on the intake air flow rate (Qair), multiplied by the stoichiometric ratio (γ); the measured signal of richness is defined by means of a wealth probe (2).

4. Process according to claim 3,

characterized in that the wealth probe (2) is of the NOx type.

5. Method according to one of claims 2 to 4,

characterized in that a mapping of the wealth drifts in the engine field is performed by the drift identifier (3), by learning any drifts; the value of the calculated richness carried by the constitutive signal of the wealth model is continuously corrected thanks to this learning.

6. Method according to one of claims 2 to 5,

characterized in that the first step of processing the Kalman filter (4) consists in applying to the first model richness input signal (0 ₂ ) the virtual alteration that it would undergo if it were picked up by the probe (2 ), this alteration consisting of:

A pure delay due to the transport of the exhaust gases from the cylinder to the probe (2), to which is added the pure delay of the probe (2) itself; and

• a filter ; a signal filtered calculated richness' ⁽⁰ ° or ^) and a state vector 'a priori' are created using the following equations, assuming that the pattern of behavior is used in ^2nd order:

θ ₀

o

[o 0 0 1] · + v ,,

with:

Θ,: the drift identified, ie the difference between the measured wealth and the calculated wealth signal passed by the behavior model of the probe;

° 2 _: calculated wealth (model); ^ 2: the filtered computed wealth (order 1) in order to take into account the behavior of the probe;

° i: the calculated computed wealth (order 2) to take into account the behavior of the probe;

the vector composed of these four variables constitutes the state vector.

0. | j _ar _C h _esse calculated (model) at time k-1;

0 | hchesse _was measured by the probe at time k;

^{Wk ~ l} : the signal noise model 'calculated wealth';

^Vk : the noise of the measured signal of richness by the probe;

each of the input signals (0 ₂ ) and the measured signal of richness (<¾ sensor) by the probe (2) is given a relative confidence by introducing process noises (w) and measurement noises (v).

7. Process according to claim 6,

characterized in that the second step of processing the Kalman filter (4), which is performed a posteriori, consists in comparing the virtual signal with the signal actually picked up and analyzed by the richness probe (2); the gap between g

these two signals define the instantaneous wealth drift (° ² ).

8. Method according to one of claims 6 and 7,

characterized in that a limitation of the confidence in the drift identification is performed by means of the Kalman gain (4), which forces a convergence time before considering the fully identified drift; the finally estimated wealth signal is therefore the model signal (0 ₂ ) recalibrated by the measurement of the probe (2) with a time of integration, linked to the necessarily limited confidence in the model of the behavior of the wealth probe (2). ).

9. Method according to one of claims 6 to 8,

characterized in that the combination of the obtained map and the Kalman filter (4) is carried out in: ^■ using drift read in the scalable mapping to directly correct the model wealth input signal (0 ₂₎ of the Kalman filter (4);

^■ using drift read for the state 'a priori' the Kalman filter (4), allowing a more or less resulted permanent pre-positioning, according to the fill level of the mapping, which is then corrected 'a posteriori' via the measured signal of richness (<¾ sensor) by the probe (2) and the Kalman gain.

10. Device for continuously estimating the cylinder richness of an internal combustion engine, said engine comprising a plurality of cylinders, at least one richness measuring probe, a plurality of injectors connected to the cylinders, a flowmeter,

characterized in that it comprises a drift identifier (3) and a Kalman filter (4) shaped to implement the method or methods according to any one of the preceding claims.