Method and device for dynamic monitoring of gas sensors

Abstract

In a method for monitoring the dynamics of gas sensors of an internal combustion engine, which gas sensors exhibit a low-pass behavior as a function of geometry, measurement principle, aging, or contamination, a dynamics diagnosis is carried out, upon a change in the gas state variable to be measured, on the basis of a comparison between a modeled and a measured signal. The parameters of the low-pass behavior are determined in direction-dependent fashion by minimizing direction-dependent error signals created by high-pass filtering and logical combination with direction-dependent saturation characteristic curves, the direction-dependent error signals being calculated by comparing the modeled and the measured signal for a rising and a falling signal component.

Claims

1. A method for monitoring the dynamics of a gas sensor of an internal combustion engine, wherein the gas sensor exhibits a low-pass behavior as a function of at least one of geometry, measurement principle, aging, and contamination, the method comprising: performing a dynamics diagnosis upon a change in a gas state variable measured by the gas sensor, on the basis of a comparison between a modeled and a measured signal; wherein the measured signal is an actual value of an output signal of the gas sensor and the modeled signal is a model value, and wherein the parameters of the low-pass behavior are determined in direction-dependent fashion by minimizing direction-dependent error signals which are created by high-pass filtering and logical combination with direction-dependent saturation characteristic curves, the direction-dependent error signals being calculated by comparing the modeled and the measured signal for a rising and a falling signal component, wherein the gas state variable for diagnosing the dynamics of the gas sensor is an air/fuel ratio of an air/fuel mixture delivered to the internal combustion engine, and wherein the air/fuel ratio is varied by a positive excitation that periodically varies the air/fuel ratio one of (i) by way of small step-like changes in an injection quantity, or (ii) by way of an oscillating control circuit.

2. The method as recited in claim 1, wherein minimization is carried out by adapting the parameters of the low-pass behavior in one of (i) a model for the gas sensor or (ii) in separate error models for the rising signal component and for the falling signal component.

3. The method as recited in claim 1, wherein excitations having a sufficiently large signal-to-noise ratio, in which the gas state variable to be measured is varied, are used for identification of the direction-dependent parameters.

4. The method as recited in claim 1, wherein a time constant, a dead time, and a gain factor are evaluated as direction-dependent parameters, in each case separately for a rising and falling signal component.

5. The method as recited in claim 1, wherein the direction-dependent error signals are calculated as difference values or squares of said difference values, the difference value being determined for a rising signal from a high-pass-filtered modeled signal for a rising value and a high-pass-filtered measured signal for a rising value, and the difference value for a falling signal being determined from a high-pass-filtered modeled signal for a falling value and a high-pass-filtered measured signal for a falling value.

6. The method as recited in claim 1, wherein the determination of the parameters of the low-pass behavior is carried out online with the aid of recursive, continuously operating optimization methods.

7. The method as recited in claim 1, wherein residual errors from the determination of the individual parameters are compared, and the error pattern having the lesser residual error is selected as the actual error pattern.

8. The method as recited in claim 6, wherein after each adaptation step, the adapted parameters are programmed into one of operating-point-dependent characteristic curves or multi-dimensional characteristics diagrams.

9. The method as recited in claim 6, wherein in the context of optimization, an adaptation rate is defined separately, by way of a learning gain, for each of the parameters to be optimized.

10. The method as recited in claim 6, wherein the monitored gas sensor is one of a gas pressure sensor, a gas temperature sensor, a gas mass flow sensor, or a gas concentration sensor used one of (i) as an exhaust gas probe in an exhaust gas duct of the internal combustion engine as part of an exhaust gas monitoring and abatement system, or (ii) in an intake air passage of the internal combustion engine.

11. The method as recited in claim 10, wherein the monitored gas sensor is an exhaust gas probe in the form of one of a broadband lambda probe or NO.sub.x sensor with which an oxygen content in a gas mixture is determined.

12. An apparatus for monitoring the dynamics of a gas sensor used one of (i) in an exhaust gas duct of an internal combustion engine as part of an exhaust gas monitoring and abatement system, or (ii) in an intake air passage of the internal combustion engine, the gas sensor exhibiting a low-pass behavior as a function of at least one of geometry, measurement principle, aging, and contamination, the apparatus comprising: a diagnosis unit performing a dynamics diagnosis upon a change in a gas state variable measured by the gas sensor, on the basis of a comparison between a modeled and a measured signal, wherein the measured signal is an actual value of an output signal of the gas sensor and the modeled signal is a model value, and wherein the diagnosis unit has at least one high-pass filter, at least one subtractor, and memory units storing direction-dependent saturation characteristic curves, and wherein the parameters of the low-pass behavior are determined in direction-dependent fashion by minimizing direction-dependent error signals which are created by high-pass filtering and logical combination with direction-dependent saturation characteristic curves, the direction-dependent error signals being calculated by comparing the modeled and the measured signal for a rising and a falling signal component, wherein the gas state variable for diagnosing the dynamics of the gas sensor is an air/fuel ratio of an air/fuel mixture delivered to the internal combustion engine, and wherein the air/fuel ratio is varied by a positive excitation that periodically varies the air/fuel ratio one of (i) by way of small step-like changes in an injection quantity, or (ii) by way of an oscillating control circuit.

13. The apparatus as recited in claim 12, wherein the diagnosis unit has memory units storing operating-point-dependent characteristic curves or characteristics diagrams.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 schematically depicts the technical environment in which the method according to the present invention can be applied.

(2) FIG. 2 is a block diagram of a dynamics diagnosis function in accordance with the invention.

DETAILED DESCRIPTION OF THE INVENTION

(3) FIG. 1 schematically shows, using the example of an Otto-cycle engine, the technical environment in which the method according to the present invention for diagnosis of an exhaust gas probe 15 can be used. Air is delivered via an air intake 11 to an internal combustion engine 10, and its mass is identified using an air mass sensor 12. Air mass sensor 12 can be embodied as a hot film air mass sensor. The exhaust gas of internal combustion engine 10 is discharged through an exhaust gas duct 18, an exhaust emission control system 16 being provided behind internal combustion engine 10 in the flow direction of the exhaust gas. Exhaust emission control system 16 usually encompasses at least one catalytic converter.

(4) An engine control system 14 is provided in order to control internal combustion engine 10, which system on the one hand delivers fuel to internal combustion engine 10 via a fuel metering system 13 and on the other hand has delivered to it the signals of air mass sensor 12 and of exhaust gas probe 15 disposed in exhaust gas duct 18, and of an exhaust gas probe 17 disposed in exhaust gas duct 18. In the example shown, exhaust gas probe 15 determines an actual lambda value of a fuel/air mixture delivered to internal combustion engine 10. It can be embodied as a broadband lambda probe or a continuous lambda probe. Exhaust gas probe 17 determines the exhaust gas composition after exhaust emission control system 16. Exhaust gas probe 17 can be embodied as a step probe or binary probe.

(5) For dynamics diagnosis of exhaust gas probe 15, the air/fuel ratio (AFR) in the combustion chamber is usually adjusted in step fashion, and within a certain time span after the step the absolute value of the maximum slope of the measured air/fuel ratio is determined. In accordance with the invention a continuously operating method is proposed especially for detecting asymmetrical dead times and time constants, which method does not evaluate individual large steps in the air/fuel ratio but rather utilizes any excitation having a sufficiently large signal-to-noise ratio. This can be, for example, the positive excitation that is generally present and that periodically varies the air/fuel ratio by way of small step-like changes in injection, or an oscillating control circuit.

(6) For detection of these asymmetrical time constants and dead times, the methods of online identification known from the literature, or further such methods described in parallel applications of the Applicant, are expanded in such a way that not only is a symmetrical time constant and dead time identified jointly for a rising and falling signal, but a time constant and a dead time are respectively identified separately for a rising and falling signal.

(7) FIG. 2 shows a block diagram 20 indicating the functionality of the method in a preferred variant of the method.

(8) Firstly the model input having a lambda value 21 .sub.mod modeled in accordance with a nominal model, and the process output to be identified, having a measured lambda value .sub.meas, are filtered with an identical high-pass filter 23.

(9) This removes a possible offset from the signals, so that the offset does not need to be explicitly estimated in the course of optimization. The high-pass filtration furthermore produces a separation into a rising and a falling signal, by the fact that the high-pass-filtered signals are logically combined with saturation characteristic curves 26, 27, 28, 29 and a separation of rising and falling signals thus takes place, a saturation characteristic curve 26 being provided for a rising model signal component, a saturation characteristic curve 27 for a rising measured signal component, a saturation characteristic curve 28 for a falling modeled signal component, and a saturation characteristic curve 29 for a falling measured signal component. This combination of high-pass filter 23 and saturation characteristic curves 26, 27, 28, 29 makes possible a distinction between signal components having a rising (positive) and falling (negative) edge, and thus the identification of asymmetrical time constants and dead times. The saturation elements can be defined as follows:
y.sub.sat,pos=x for x0(3a)
y.sub.sat,pos=0 for x<0(3b)
for saturation characteristic curves 26, 27
y.sub.sat,neg=0 for x>0(3c)
y.sub.sat,neg=x for x0(3d)
for saturation characteristic curves 28, 29.

(10) The result of this high-pass filtering and subsequent signal separation by way of saturation characteristic curves 26, 27, 28, 29 for the modeled and measured lambda value 21, 22 is that ultimately four signals are available: i. a high-pass-filtered modeled lambda value for a rising lambda value .sub.mod,pos, ii. a high-pass-filtered modeled lambda value for a falling lambda value .sub.mod,neg, iii. a high-pass-filtered measured lambda value for a rising lambda value .sub.meas,pos, iv. a high-pass-filtered measured lambda value for a falling lambda value .sub.meas,neg,

(11) Using these signals, a separate identification of gain K, dead time T.sub.t, and/or time constant T is then performed for rising and falling signals. Methods known from the literature are utilized in this context in continuous or discrete time; continuous methods have the advantages recited above.

(12) In a preferred variant, the identification is therefore accomplished online with the aid of recursive, continuously operating optimization methods, so that no storage of the signals is necessary.

(13) Identification is based on a comparison of the modeled and measured signal, separately for rising and falling signal components; using subtractor 30, a respective difference is formed and that difference is minimized, the gain K, dead time T.sub.t, and/or the time constant being the parameters to be optimized. These differences are defined, as error values for a rising signal and a falling signal 31, 32 (e.sub.pos, e.sub.neg), as follows:
e.sub.pos=.sub.meas,pos.sub.mod,pos(4a)
e.sub.neg=.sub.meas,neg.sub.mod,neg(4b)
and are then respectively calculated with a squaring unit 33 to yield an error indication for the rising signal and for the falling signal 34, 35 (E.sub.pos, E.sub.neg), as follows:
E.sub.pos=(e.sub.pos).sup.2(5a)
E.sub.neg=(e.sub.neg).sup.2(5b).

(14) This squared error value (error indication) represents a quality criterion on the basis of which the lambda model can be adapted, directly and/or additionally via error models 24, 25, for the rising and the falling signal (FM.sub.pos, FM.sub.neg) by way of a parameter adaptation for the rising and falling signal 36, 37, where the respective error model 24, 25 can be provided in the function sequence after high-pass filter 23 as shown in FIG. 2, or also before high-pass filter 23. An adaptation of the time constant T.sub.pos, dead time T.sub.tpos, and/or gain K.sub.pos is provided for in the parameter adaptation for the rising signal 36. The parameter adaptation for the falling signal 37 provides for adaptation of the time constant T.sub.neg, dead time T.sub.tneg, and/or gain K.sub.neg.

(15) Optimization can be carried out, for example, using gradient methods such as the steepest slope method, or using the Gauss-Newton method, these also being available in recursive variants for online optimization. The gradients are calculated analytically by filtration and dead-time delay of the modeled and measured signals.

(16) It is furthermore possible in the context of optimization to define the adaptation rate separately, by way of learning gains, for each of the parameters to be optimized.

(17) Because this method carries out, at every time interval, an adaptation of the parameters to be optimized, the adapted parameters can furthermore be programmed into operating-point-dependent characteristic curves or multi-dimensional characteristics diagrams at the current point in time, so that identification as a function of operating point is also possible. For this, at each time interval the current parameters of the error model 24, 25 (FM.sub.pos, FM.sub.neg) are read out of the characteristic curves or characteristics diagrams on an operating-point-dependent basis, the adaptation is carried out based on those parameters, and the re-adapted values are programmed back into the characteristic curves or characteristics diagrams on an operating-point-dependent basis.

(18) In principle, the invention is not limited to systems whose dynamic behavior, as mentioned previously, can be described by a first-order low-pass. This identification method is likewise also applicable to systems of any order, with and without dead time.