This invention relates to a method for knock detection in a combustion engine, in which combustion is controlled by a control unit, which comprises the steps of,
From John B. Heywood, Internal Combustion Engine Fundamentals, McGraw-Hill International Ed., Automotive Technology Series, 1988, p 707 it can be read: “Knock in a spark ignited engine is the spontaneous ignition of the unburned “end-gas” ahead of the flame as the flame propagates across the combustion chamber. It results in an increase in gas pressure and temperature above the normal combustion levels. Knock results in increased local heat fluxes to regions of the piston, the cylinder head, and liner in contact with the end-gas. Increases to between twice and three times the normal heat flux in the end-gas region has been measured. It is thought that the primary knock damage to the piston in this region is due to the combination of extremely high local pressures and higher temperatures.”
Knock in internal combustion engines can be measured using a pressure sensor. Let x(k) denote the pressure sensor measurement at combustion cycle k. A measure of the knock intensity can be taken as the maximum amplitude of the band-pass filtered pressure signal, see John B. Heywood, Internal Combustion Engine Fundamentals, McGraw-Hill International Ed., Automotive Technology Series, 1988, pp 450 for further details.
The pressure signal is often used as a reference, i.e., it is considered to be the “true” measurement of the knock intensity. However, pressure sensors are expensive and have a short lifetime. Therefore they are not often used in mass production but instead replaced by cheaper sensors. Let yl(k) denote measurements from such sensors that are not as accurate as the reference (pressure) sensor, where l=1, . . . , L is the sensor index.
A model of the measurements from the sensors can be formulated as
y1(k)=α1·x(k)+x(k)·e1(k)=x(k)(α1+e1(k))
where
In a real engine x(k) may assume a continuum of values according to some distribution that describes the behaviour of the sensor. However, in order to be able to determine the probability of false alarm and probability of detection when using a detection strategy we need to confine the values that x(k) can take. Therefore, let the values of x(k) for a normal and knocking cycle, respectively, be defined as follows
There are a number of sensors that produce data that can be used to compute the knock intensity from combustion in a cylinder. The pressure sensor was mentioned above as a commonly used reference sensor in which case the knock intensity can be computed as the maximum amplitude of the band-pass filtered pressure trace. Another sensor is the accelerometer, which senses above-normal vibration levels on the cylinder head at the characteristic knock frequency. A third sensor is the ion-current measurement system, e.g. as proposed in U.S. Pat. No. 5,803,047 and U.S. Pat. No. 5,992,386. Then, again, the knock intensity is derived based on a characteristic frequency pretty much using the same basic principles as in the case of accelerometers and pressure sensors.
However, there are other features in the ion-current trace that is correlated to the knock intensity. As cited in the introduction, and e.g. U.S. Pat. No. 5,803,047 and U.S. Pat. No. 5,992,386 it is known that the heat transfer to the engine increases and as a consequence thereof the temperature in the cylinder increases. A temperature rice will increase the ionization level in the cylinder which in turn will lead to an increase in the level of the ion-current. Hence, in combination with a knocking cycle, it is to be expected that the area under the ion-current trace, i.e., the ion-current integral, will increase as compared to a normal combustion. This increase in ion-current may be observed during a knocking cycle and/or in consecutive cycles.
In summary, we have a number of potential measurements available for knock detection, denoted by yl(k) as defined above. For example, l may refer to an ion sensor, wherein knock intensity measurements is based on a characteristic knock frequency in the ion-current. Moreover l may again refer to an ion sensor, wherein instead the integral (sum) of the ion-current trace over a certain interval is used to measure knock intensity. As a final example l may refer to a measurement from an accelerometer.
Classical knock detection as in U.S. Pat. No. 5,803,047 and U.S. Pat. No. 5,992,386 is typically based on one of the aforementioned sensor measurements. Then the knock sensor output is compared to a threshold and a knock alarm is alerted if the knock intensity is greater than the threshold, i.e., typically a knock detection algorithm has the following structure
Of course, if the sensor is not a reference (pressure) sensor, then the choice of the threshold T together with the noise and signal characteristics determine the probability of false alarm, correct detection, and correct rejection and missed detection; see Steven M. Kay, Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, Prentice Hall Signal Processing Series, for further details. For example, if T is increased, then the probability of false knock alarm will decrease at the expense of an increased probability of missed knock detection. There is a trade-off when choosing T and which T to choose depends on the application. The threshold T may also be made data dependent. For example, it may be a function of an estimate of the noise variance. However, such a strategy may lead to complications if the noise variance depends on the engine working point (load and engine speed). To illustrate this point, consider a case when the noise is high at one working point and low in another. When the engine has been run at a working point with a high noise level and the detection algorithm is tuned to a predetermined probability of false alarm, then the detection strategy will be “blind” with respect to knocks when the engine switches to a working point characterized by a low noise level due to the latency in the estimate of the noise variance. This phenomenon may seriously damage the overall performance of the knock detection system and be harmful to the engine.
In U.S. Pat. No. 5,803,047 the above problem is partly handled by using correction parameter that is combined with the detection variable in conjunction with performing the control. Hence, prior art, e.g. U.S. Pat. No. 5,803,047, uses a method for knock detection in a combustion engine, in which combustion is controlled by a control unit, which comprises the steps of,
However, such a control strategy is not very reliable and does not perform well in dynamic environments, e.g. in “blind” detection situations.
The above mentioned deficiency/problem is eliminated or at least minimized by means of the invention which relates to a method for knock detection in a combustion engine, in which combustion is controlled by a control unit, which comprises the steps of,
Thanks to the invention improved detection characteristics is achieved which in turn leads to improved possibilities of achieving high quality control of a combustion engine.
According to further aspects of the invention:
In the following the invention will be described more in detail with reference to the enclosed figures, wherein:
In
According to the invention an improved control strategy may be achieved since the obtained detection variable z1 is based on a combination of two sub-values y1, y2. Accordingly the first processing unit 5 is used to compute two different sub-values, by means of the in data from the sensor signal 4 (see
Form a new detection variable;
Then form a new detection based on this new (combined) test variable. If the observations y1 (k) are correlated with the knock intensity, then z(k) will have improved detection characteristics as compared to when using only a subset of the available detection variables y1(k). In
In
In
In
In
As stated above the detection can be made “blind” if the quality of the sensor output varies with the engine working point. This drawback can be alleviated by estimating necessary descriptive statistical entities and compute and possibly vary the threshold value TT′ accordingly (indicated as a dotted line in
The amplification parameters α1 and the corresponding noise variances E[e12(k)]=σ12 can be estimated using the reference measurements
The estimates {circumflex over (α)}1 and {circumflex over (σ)}12 can be used to determine the threshold T to, e.g., give a certain probability of false alarm suitable for a certain application. Estimate the amplification factors and the noise variances over a selected grid of working points. Then the threshold can be made working point dependent.
In the case when multiple sensors are available (and not only multiple computations from a single sensor such as ion-current) it may be possible to estimate the amplification factors and corresponding noise variances from real-time data, thus yielding an adaptive mode of the threshold T.
Optimal scaling factors λl can be determined using numerical simulations or from analytical expressions when this is feasible.
The invention is not delimited by the above given examples but may be varied within the scope of the enclosed claims. For instance, it is evident for the skilled person that the control unit 2 may be achieved in many different manners, e.g. not having a single unit 2 for all of the components 5, 6, 7, 8 for performing the desired computations/—activities, but instead having a distributed set of components. Moreover it is evident for skilled person that the described components are schematically presented and that the actual content of the control arrangement 2 may vary a lot depending on the desired needs of the actual application for the invention. Further it is evident for the skilled person within the field that the presented models for computation of different parameters is by way of example only, i.e. also other models may be used, e.g. the model for computing the amplification factor may instead be based on a more sophisticated noise model, etc. Finally it is perceived that the invention may also be applied in connection with detection/control of other combustion phenomena, e.g. misfire, unacceptable AFR, i.e. detection of deviation from a preferred value of a combustion parameter.
Number | Date | Country | Kind |
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0502190 | Oct 2005 | SE | national |
Filing Document | Filing Date | Country | Kind | 371c Date |
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PCT/SE2006/050297 | 8/28/2006 | WO | 00 | 4/25/2008 |
Publishing Document | Publishing Date | Country | Kind |
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WO2007/040447 | 4/12/2007 | WO | A |
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Number | Date | Country | |
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20100168990 A1 | Jul 2010 | US |