Abstract:
A method and a device for the dynamic monitoring of an air charging system of an internal combustion engine, subsystems of the air charging system having a low-pass characteristic, and a characteristic state quantity that is to be measured being compared with a modeled, identical state quantity. The measured signal and the modeled signal are filtered using a high-pass filter or bandpass filter, and, given a change in the characteristic state quantity that is to be measured, higher-frequency signal portions are evaluated, which is advantageous with regard to the recognition of so-called slow response errors.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention relates to a method and to a device for the dynamic monitoring of an air charging system of an internal combustion engine, subsystems of the air charging system having a low-pass characteristic as a function of geometry, measurement design, aging, or contamination, and, in the case of a change in a characteristic state quantity that is to be measured, a dynamic diagnosis being carried out on the basis of a comparison of a modeled signal and a measured signal, the measured signal being an actual value of the characteristic state quantity that can be measured directly or calculated from measurement values of other quantities, and the modeled signal being a model value of the characteristic state quantity. In addition, the present invention relates to a device for carrying out the method. 
       BACKGROUND INFORMATION 
       [0002]    Legal regulations concerning on-board diagnosis (OBD) in motor vehicles having an internal combustion engine require the recognition of emission-relevant errors in subsystems of the air charging system. Thus, legislation in California requires monitoring of the exhaust gas recirculation (EGR) in diesel engines and monitoring of the charge pressure regulation in gasoline and diesel engines having chargers, e.g. having exhaust gas turbochargers. European legislation requires the monitoring of the exhaust gas recirculation in diesel engines. In California legislation, inter alia the recognition of so-called slow response errors, relevant to emissions, is required. These are understood as a retarded response of the exhaust gas recirculation, or of the charge pressure, to a change in the target value, which can result in an increase in the exhaust gas emissions, up to values above OBD boundary values. 
         [0003]    A retarded response of the exhaust gas recirculation can cause, for example in a diesel engine, an increase in emissions if a temporarily too-low EGR rate results in an increased combustion temperature and an increased oxygen excess, and thus an increase in nitrogen oxide emissions. In contrast, a temporarily too-high EGR rate can result in a reduced combustion temperature and a reduced oxygen excess, and thus an increase in soot emissions. A temporarily too-low or too-high charge pressure can cause disturbances in the air charging of the cylinders, so that the quantity or the time of the fuel injection or the EGR rate is no longer optimally adapted to the actual air charging, which can cause an increase in the exhaust gas emissions. 
         [0004]    Various methods exist for the diagnosis of subsystems of the air charging system. For example, methods are known that monitor the intervention of the charging regulation when there is a change in the EGR target value. A stronger intervention of the charging regulation indicates a retarded response of the exhaust gas recirculation. Likewise, methods are known that model the charge pressure under the assumption of an error-free system and compare the model value to the measured charge pressure. If, when there is a change in the charge pressure target value, a large difference is recognized between the model value and the measured value, this is evidence of a retarded response of the charge pressure regulation. 
         [0005]    Patent document DE 10 2011 088 296 A1 discusses a method and a device for carrying out the method for dynamic monitoring of gas sensors of an internal combustion engine, the gas sensors having a low-pass characteristic as a function of geometry, measurement design, aging, or contamination, and, given a change in the gas state quantity that is to be measured, a dynamic diagnosis being carried out on the basis of a comparison of a modeled signal and a measured signal, the measured signal being an actual value of an output signal of the gas sensor and the modeled signal being a model value. Here, it is provided that the output signal of the gas sensor is filtered using a high-pass filter, and, when there is a change in the gas state quantity that is to be measured, higher-frequency signal portions are evaluated. With this method, changes with regard to the dynamic behavior in gas sensors can be detected and quantified. 
         [0006]    A related method, discussed in DE 10 2012 201 033 A1, can be used to ascertain a dead time of gas sensors. 
         [0007]    The approaches discussed in DE 10 2011 088 296 A1 and DE 10 2012 201 033 A1 also offer approaches for providing a dynamic monitoring of an air charging system of an internal combustion engine with corresponding adaptation of the method. 
       SUMMARY OF THE INVENTION 
       [0008]    An object therefore arises of providing, in the context of a further development of DE 10 2011 088 296 A1, a dynamic monitoring of an air charging system that can meet the legal requirements cited above. An object of the present invention is also to provide a corresponding device for carrying out the method. 
         [0009]    The object relating to the method is achieved in that the measured signal and the modeled signal are filtered using a high-pass or bandpass filter, and, when there is a change of the characteristic state quantity to be measured, higher-frequency signal portions are evaluated. Subsystems in air charging systems of internal combustion engines have a typical low-pass characteristic that is a function, inter alia, of the geometry of their design. In addition, such systems can change their response characteristic due to aging or external influences. In the time domain, the decreasing limit frequency is expressed as a greater rise time, i.e., given unchanged excitation the signal edges become flatter. Therefore, if for example a suitable high-pass filter, e.g. a first-order high-pass filter, is connected in series with the subsystem to be monitored of the air charging system, then given steep changes in the state quantity to be measured, such as an air mass flow, at the output signal of the high-pass it can be recognized whether the boundary frequency of the low-pass is greater than or smaller than the boundary frequency of the high-pass. If the subsystem reacts slowly as a result of aging or external influences, then when there are changes of the gas state quantities only small, or no, higher-frequency signal portions are still determined. If the system has a high degree of dynamic behavior, this has an effect on a relatively large higher-frequency signal portion, so that with this feature a dynamic diagnosis can be realized. With the method presented here, a uniform dynamic monitoring design can be realized for subsystems of an air charging system. On the one hand, it has a high degree of robustness against disturbances such as statistical fluctuations, but also against possible offsets. On the other hand, the method is distinguished by its low degree of complexity and by low application outlay and resource requirement, such as computing outlay and storage space. 
         [0010]    In order to enable a distinction to be made between a slow system and an inadequate excitation, the speed of change of the state quantity to be measured must be assessed without using the signal of the system to be monitored itself. In a method variant, therefore, the higher-frequency signal portions of the measured signal, or of the signal calculable from measurement values of other quantities, are compared to correspondingly filtered and modeled signals, and on the basis of the comparison the dynamic characteristic of the subsystems of the air charging system is inferred. 
         [0011]    In a method variant, it is therefore provided that both the higher-frequency signal portions of the measured signal, or of the signal calculable from measurement values of other quantities, and also the filtered and modeled signals are squared and integrated, and in this way higher-frequency energy portions are calculated, and subsequently these energy portions E mod  and E meas  are set into a ratio, and on the basis of the energy ratio calculated in this way the dynamic characteristic of the subsystems of the air charging system is inferred through comparison with an applicable threshold value. The smaller the surface under the squared output signal of the high-pass is, the slower is the sensor, or the excitation. Alternatively to the signal energies, quantities that are closely associated with the signal energies can also be formed and set into a ratio. For example, instead of the signal energy the root of the signal energy can also be used. 
         [0012]    In the special case, when the threshold value for the formed energy ratio E meas /E mod  is fallen below, an impermissible dynamic characteristic of the subsystem of the air charging system can be diagnosed. Alternatively, a reciprocal value of the energy ratio can be used for the evaluation. 
         [0013]    So that multiplicative errors do not falsify the signal comparison, a norming may be carried out of the respective energy portions. Additive errors do not have an effect, because a high-pass suppresses the direct portion of a signal. 
         [0014]    In order to increase the robustness relative to an imprecisely modeled dead time of the modeled signal, it can be provided that in the ascertaining of the signal energies the integration of the higher-frequency signal portions is carried out in integration intervals that are individual for both signals, the time for the start of the integration of the respective signal, given a rising or falling signal edge of the output signal, i.e. of the unfiltered signal, being triggered depending on whether the dynamic behavior is monitored in the positive or the negative direction. In order to ensure that the edges of the measured and of the modeled signal derive from the same excitation, the trigger may be initiated after an applicable phase with constant signals. 
         [0015]    In order to recognize, in direction-specific fashion, a retarded response when there is an increase or decrease in the characteristic state quantity, as an extension it can be provided that the energy calculation and formation of the energy ratio is limited to the positive and/or negative portions of the filtered signals. For this purpose, the filtering must be configured such that a rising/falling edge of the unfiltered signal corresponds to a positive/negative portion of the filtered signal. 
         [0016]    If it has been ensured that an integration interval includes many edges and is significantly larger than possible dead time errors, the method according to the present invention can then be simplified. It is then alternatively possible to start the integration in the two paths at an arbitrary time and to carry it out for the duration of the integration. The precondition for this is only an adequate excitation due to changes of the state quantity to be measured. Thus, the time interval can include a plurality of falling and rising edges. This method can be applied particularly simply if rising and falling edges of the sensor signal do not have to be monitored separately. 
         [0017]    In a method variant, it is provided that the filter time constant of the filter and/or of the threshold value are defined and/or updated or adapted as a function of the subsystem to be monitored of the air charging system. In this way, the dynamic diagnosis can be adapted to system-specific particular features and/or to particular operational phases of the internal combustion engine. 
         [0018]    The diagnostic method according to the present invention can be used particularly advantageously in internal combustion engines, e.g. diesel engines, in which, as characteristic state quantity, an exhaust gas recirculation (EGR) mass flow through an exhaust gas recirculation (EGR) valve of the internal combustion engine is monitored, the EGR mass flow being determined through modeling on the one hand and on the other hand being directly measured or calculated from measurement values for an air mass flow in the supply air region of the internal combustion engine, from the rotational speed and from an inlet pressure or charge pressure, and the functioning of the exhaust gas recirculation valve being monitored using the dynamic diagnostic method. In this way, in particular errors of the EGR can be diagnosed early. Also advantageously, the method can be used in the monitoring of the charge pressure regulation in gasoline engines and diesel engines having chargers, for example having exhaust gas turbochargers. 
         [0019]    The object relating to the device is achieved in that, in order to carry out the method according to the present invention, a diagnostic unit is provided that has high-pass filters for evaluating higher-frequency signal portions, and at least one calculating unit for the characteristic state quantity determined through modeling, as well as calculating units such as for example integration units, comparators, and, if warranted, characteristic map units for carrying out the dynamic diagnosis, in accordance with the method variants described above. The functionality of the diagnostic unit can here be at least partly software-based; this can be provided as a separate unit or as part of a higher-order engine controlling unit. 
         [0020]    In the following, the present invention is explained in more detail on the basis of an exemplary embodiment shown in the Figures. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0021]      FIG. 1  shows, in a schematic representation, the technical environment in which the method according to the present invention can be used. 
           [0022]      FIGS. 2 a  and 2 b    respectively show a diagram of the curve for an error-free air charging system and for an air charging system that has a response characteristic retarded by approximately 2 seconds. 
       
    
    
     DETAILED DESCRIPTION 
       [0023]      FIG. 1  shows, in a schematic representation, the technical environment in which the method according to the present invention can be applied. Shown as an example is an internal combustion engine  10 , configured as a diesel engine, having an air supply  30  and an exhaust gas train  20 . The representation is limited to the parts essential for the description of the present invention. Along a supply air duct  36  of air supply  30 , fresh air  31  is supplied to internal combustion engine  10  via a hot film air mass sensor  32 , a compressor  12  of a turbocharger  11 , and a fresh air throttle  34 . Air supply  30  is subdivided into a supply air low-pressure region  14  before compressor  12 , and a supply air high-pressure region  15 , in the direction of flow, after compressor  12 . 
         [0024]    Exhaust gas  26  of internal combustion engine  10  is emitted to the surrounding environment via exhaust gas train  20  from internal combustion engine  10 , along an exhaust gas duct  21 , via an exhaust gas turbine  13  of turbocharger  12 , a particle filter  22 , and exhaust gas valve  24 , and a muffler  25 . Exhaust gas train  20  is subdivided into an exhaust gas high-pressure region  17  and an exhaust gas low-pressure region  16 , the exhaust gas low-pressure region  16  beginning after exhaust gas turbine  13  in the direction of flow. 
         [0025]    In the depicted example, a low-pressure exhaust gas recirculation line  40  connects exhaust gas low-pressure region  16  after particle filter  22  to supply air low-pressure region  14  after hot film air mass sensor  32 . Low-pressure exhaust gas recirculation line  40  contains an exhaust gas filter  41 , a first exhaust gas recirculation cooler  42 , and a low-pressure exhaust gas recirculation valve  43  having a differential pressure sensor  44  over which a low-pressure exhaust gas recirculation mass flow  49  is conducted. The pressure of the exhaust gas  26  before exhaust gas valve  24  is determined by a first pressure sensor  23 . The pressure of fresh air  31  is determined by a second pressure sensor  33  before fresh air throttle  34 , and by a third pressure sensor  35  after fresh air throttle  34 . Second and third pressure sensor  33 ,  35  can also be realized, in a different specific embodiment, as a differential pressure sensor. 
         [0026]    A high-pressure exhaust gas recirculation line  45  (EGR) connects exhaust gas high-pressure region  17  before exhaust gas turbine  13  to air supply high-pressure region  15  after fresh air throttle  34 , via a second exhaust gas recirculation cooler  46  and a high-pressure exhaust gas recirculation valve  47 , so that an EGR mass flow  48  can be recirculated. 
         [0027]    During operation, the mass of the fresh air  31  supplied to internal combustion engine  10  is determined using hot film air mass sensor  32 . Via low-pressure exhaust gas recirculation line  40 , fresh air  31  is mixed with a substream of exhaust gas  26 . The resulting air mixture is compressed by compressor  12  of turbocharger  11 , and subsequently a further substream of exhaust gas  26  is mixed with this air mixture via high-pressure exhaust gas recirculation  45 . The resulting mixture is supplied to internal combustion engine  10 . The resulting exhaust gas  26  drives turbocharger  11  via exhaust gas turbine  13 , and in so doing is relaxed to a lower pressure level. Subsequently, particles are filtered out from exhaust gas  26  by particle filter  22 . 
         [0028]    In the method according to the present invention, a quantity is modeled that is characteristic for the dynamic behavior of the monitored subsystem. For example, EGR mass flow  48  and/or low-pressure exhaust gas recirculation mass flow  49  is a characteristic quantity for the exhaust gas recirculation. In addition, the quantity has to be measurable, or derivable directly from measurement values. This holds for EGR mass flow  48 , which results from the measurement values air mass flow via the throttle valve (fresh air throttle valve  34 ), rotational speed, and inlet pressure (charge pressure), e.g. in supply air high-pressure region  15 . 
         [0029]    Through high-pass filtering or bandpass filtering, a frequency portion is extracted from the measured signal and from the modeled signal, each portion having high frequency such that it is already sufficiently attenuated by an emissions-relevant attenuation, for example the region above an angular frequency of 0.5 s −1 , in which a low-pass having a time constant of 2 seconds or greater already brings about an attenuation to 70% or less. Subsequently, the energy of the filtered signals is determined over a specified time interval by squaring the signals and integrating them. 
         [0030]    The energy values of the measured signal and of the modeled signal are compared. If the quotient measured signal/modeled signal is below an applicable threshold, a dynamic error, e.g. a slow response error, is diagnosed. 
         [0031]    The present invention has been described with reference to the example of EGR mass flow  48 , but can also correspondingly be applied to mass flow  49  of the low-pressure EGR. The EGR mass flow can be modeled well through the following throttle equation: 
         [0000]        {dot over (m)}   mod   =A /( R   spec   ×T ) 0.5 ψ( p   ds   /p   us   ,K ) p   us   (1)
 
         [0000]      where 
         [0000]      ψ( x,K )=( Kx   1/K ( x   1/K   −x )/( K− 1)) 0.5   (2a)
 
         [0000]      for 
         [0000]      (2/( K+ 1)) 1/(K-1)   &lt;x&lt; 1 
         [0000]      and 
         [0000]      ψ( x,K )=(2/( K+ 1)) 1/(K-1) ( K/K+ 1)) 0.5   (2b)
 
         [0000]      for 
         [0000]        x≦ (2/( K+ 1)) 1/(K-1)    
         [0000]    where
 
{dot over (m)} mod =modeled EGR mass flow
 
p us =pressure upstream from the EGR valve
 
p ds =pressure downstream from the EGR valve
 
T=temperature
 
A=effective cross-section of the EGR valve (function of the controlling)
 
R spec =specific gas constant of the exhaust gas=289 J/(kg K)
 
K=adiabatic coefficient of the exhaust gas=1.4
 
         [0032]    The model holds under the assumption of an error-free system. However, if the response of the exhaust gas recirculation is retarded, for example due to wear of EGR valve  47 , then the real EGR mass flow is retarded relative to the modeled EGR mass flow {dot over (m)} mod . The real EGR mass flow can be ascertained from the measurement values air mass flow via throttle valve, rotational speed, and inlet pressure (charge pressure). 
         [0033]      FIGS. 2 a  and 2 b    each show, in a curve diagram  50 , the comparison between the modeled and measured EGR mass flow  51 ,  52 . In addition, diagrams  50  show vehicle speed  53 . Mass flow  55  and speed  56  are shown as ordinate, and time  54  is shown as abscissa.  FIG. 2 a    shows the behavior in an error-free system.  FIG. 2 b    shows the curve in a system in which the EGR valve has been artificially damped with a time constant of 2 seconds. 
         [0034]    A low-pass having a small time constant for noise suppression, and a high-pass having a time constant in the emissions-relevant range (e.g. 2 seconds) is applied to the modeled and to the measured EGR mass flow signal: 
         [0035]    Low-Pass: 
         [0000]        {dot over (m)}   lp(n) =(1− T   s   /T   lp ) {dot over (m)}   lp(n-1)   +T   s   /T   lp   {dot over (m)}   (n)   (3)
 
         [0036]    Subsequent High-Pass: 
         [0000]        {dot over (m)}   hp(n) =(1− T   s   /T   hp )( {dot over (m)}   hp(n-1)   +{dot over (m)}   lp(n)   −{dot over (m)}   lp(n-1) )  (4)
 
         [0000]    where:
 
n=n=1, 2, 3, . . . number of the discretization step
 
{dot over (m)} (n) =unfiltered mass flow
 
{dot over (m)} lp(n) =low-pass-filtered mass flow
 
{dot over (m)} hp(n) =low-pass-high-pass-filtered mass flow
 
T s =discretization interval (time)
 
T lp =low-pass time constant
 
T hp =high-pass time constant
 
         [0037]    The filtered signals are subsequently squared, and are integrated over a specific time interval: 
         [0000]        E   mod   [n   start   ,n   end ]=sum(( {dot over (m)}   mod,hp(n) ) 2 )  (5a)
 
         [0038]    from n=n start  to n end    
         [0000]        E   meas   [n   start   ,n   end ]=sum(( {dot over (m)}   meas,hp(n) ) 2 )  (5b)
 
         [0039]    from n=n start  to n end    
         [0000]    where
 
[n start , n end ]=time interval
 
{dot over (m)} mod,hp =filtered modeled signal
 
{dot over (m)} meas,hp =filtered measured signal
 
E mod =energy of the filtered modeled signal
 
E meas =energy of the filtered measured signal
 
         [0040]    In the error-free case, E mod  and E meas  are approximately equal. If the response of the EGR is retarded, then E meas  is smaller than E mod . A slow response error can be recognized by comparing the quotient E meas /E mod  with a threshold value. 
         [0041]    As an alternative to an error-free system, a limited system can be modeled that does not result in exceeding of the emission limits. In general, E meas  is then significantly greater than E mod . E meas  is smaller than E mod  only in the case of an errored real system. 
         [0042]    In a further embodiment of the method, it is provided that instead of the energy ratio a quantity derived therefrom, for example the reciprocal value, is compared with a threshold value. 
         [0043]    In order to recognize a direction-specific retarded response given increase or reduction in the EGR mass flow, the energy formation can be reduced to the positive or negative portions of the filtered signals: 
         [0000]        E   mod,pos   [n   start   ,n   end ]=sum((max(0, {dot over (m)}   mod,hp(n) )) 2 )  (6a)
 
         [0044]    from n=n start  to n end    
         [0000]        E   meas,pos   [n   start   ,n   end ]=sum((max(0, {dot over (m)}   meas,hp(n) )) 2 )  (6b)
 
         [0045]    from n=n start  to n end    
         [0000]        E   mod,neg   [n   start   ,n   end ]=sum((min(0, {dot over (m)}   mod,hp(n) )) 2 )  (6c)
 
         [0046]    from n=n start  to n end    
         [0000]        E   meas,neg   [n   start   ,n   end ]=sum((min(0 ,{dot over (m)}   meas,hp(n) )) 2 )  (6d)
 
         [0047]    from n=n start  to n end