DE3539395A1 - METHOD AND DEVICE FOR ADAPTING THE MIXTURE CONTROL IN INTERNAL COMBUSTION ENGINES - Google Patents

Description

State of the art

The invention is based on a method according to the Genus of the main claim and a facility according to the genus of the claim. The invention builds on the applicant's earlier application P 35 05 965.6 where the adaptive feedforward control is both structural certain areas of a basic map as also about a global factor everyone from the map tax value obtained multiplicative in the sense of a Displacement of all map support points influenced.

In the patent application which also goes back to the applicant P 34 08 215.9 is suggested in a map stored and depending on  Operating parameters of the internal combustion engine selected To change values according to a learning process that not just a single predetermined Map value, but also those in its environment respective map values depending on the change of the respective value additionally modified will. To a learning process in the field of feedforward control To be able to introduce an actual value is required about the actual operating state of the internal combustion engine, usually the control factor as the actual value or the manipulated variable of a lambda controller is evaluated becomes. This manipulated variable of the lambda controller influences therefore in the sense of adaptive learning of the input control area (for example injection time map with evaluation of a throttle valve signal and the Engine speed) this pilot range and also serves as a feedback actual value at current regulation of the mixture control on the basis those that may have been changed through adaptive learning Pre-control values from the map area.

In detail, it can be done so that the issued by the lambda controller Correction factor averaged, suitable boundary conditions subjected and then both in a superimposed on the basic map structural map (structure adaptation) as well incorporated into a global factor (global adaptation) becomes. This familiarization then takes place on leaving a catchment area defined around each map support point (Adaptation area).  

It is generally known in this context (DE-OS 28 47 021; GB-PS 20 34 930 B), mixture metering systems so train that the dosage or metering of the Fuel, for example via so-called learning control systems he follows. Such a learning control system contains in a permanently active read-write memory for example values for the injection, which at Operation of the machine are available. The maps result in a fast reacting feedforward control, for example for the Injection quantity or generally for the fuel metering or for others, changing as quickly as possible Operating parameters to be adapted to the operating conditions on the internal combustion engine, such as ignition timing, exhaust gas recirculation rate u. Like. To learn control systems the individual map values can depend on the operating parameters corrected and in the respective Memory can be written.

The following explanations are based on the findings and the disclosure content of the earlier application P 35 05 965.6. The subject of the present application relates focus on further improvements in the area the structural and global adaptation in such Fuel metering systems.

The invention is therefore based on the object adaptive learning processes with changing external conditions self-adapting maps for the  Improve fuel delivery in internal combustion engines and ensure that the corrective action is in the desired Way in the structural and the global Adaption is distributed and a reference to the actual Operating behavior of the internal combustion engine (Frequency of the manipulated variable vibrations) also in the course of includes adaptive learning.

Advantages of the invention

The invention solves this problem with the characterizing Features of the main claims 1 and 2 or of claims 8 and 9 and has the advantage that through different quantization of the factors for structural adaptation and global Adaptation the learning ability is divided so that at Assumed stable structure in the engine area outer, slowly changing operating conditions (e.g. changing air pressure) essentially be compensated for by the global factor and do not affect the structural area. On the other hand enables the invention by a different Weighting between global and structural adaptation such a division that where effective structural a correction is required, this through the structural factor in the respective map area made and to a lesser extent the global factor is struck.

Finally, it is advantageous that, according to one embodiment, the information about the manipulated variable of the lambda controller, the formation of the structural and global factor and further work processes are switched off in the grid of jumps in the manipulated variable of the lambda controller, that is, based on the number of probe passes. so that the adaptation is dynamically adapted to the operating variable λ and does not run asynchronously in any time grid. The factors can therefore be optimally determined.

Furthermore, this relationship to the manipulated variable Jump grid of the lambda controller the interpretation of the Adaptation as self-locking, because when the lambda Controller runs to one of its setting stops, so none Grid jumps take place, also not further adapted can be and in any case first the manipulated variable is recognized as implausible.

By the measures listed in the subclaims are advantageous developments and improvements to Invention possible.

drawing

Embodiments of the invention are shown in the drawing and are explained in more detail in the following description. In the drawings Fig. 1 schematically shows a block diagram of the active learning area with evaluation of the control variable curve of the lambda regulator and division into structural and global adaptation, Fig. 2 shows the range of an adaptation surface with inlet and outlet timing of the operating parameters, and Fig. 3 in the form a diagram the time sequence of the lambda controller manipulated variable.

Description of the embodiments

The various forms and variants of the present invention complement the basic ideas explained in detail in the main application in several respects, namely the type and division of the adaptation to a structural map which overlaps the basic map, and to the area of the global factor and the relationship between the factor formation Determination of the manipulated variable, the counting of settling delays u. Like. On the jump behavior of the lambda probe (manipulated variable jumps of the lambda controller), so that the adaptation is dynamically adapted to the operating parameter λ .

To understand the present invention, it is necessary know the subject of the main application, this, as already mentioned, assumes with full disclosure of the content there Versions and features also in this application.

It is also noted that the in the drawings each illustrated, the invention and its different aspects based on discrete switching levels or block-indicating agents do not limit the invention, but in particular serve to be functional To illustrate basic effects and special ones Functional sequences in a possible form of implementation specify. It goes without saying that the individual building blocks, Components, blocks, functions and. Like. in analog, digital or even hybrid technology can be or also, in whole or in part, corresponding areas of program-controlled  digital systems or programs, for example realized by microprocessors, microcomputers, digital logic circuits u. Like. Because the professionals The help of program experts is always available stands, the corresponding functional sequences, commands and implement effects in a suitable program language can, this step is no longer in need of explanation viewed and on the additional representation of flowcharts when applied through Microprocessors than available for understanding Invention dispensed with. The one given below Description of the invention is therefore merely a preferred embodiment with respect to functional overall and timing, which by the respective blocks achieved effect and regarding the respective interaction of the through the individual components shown sub-functions to be evaluated, the references to the circuit blocks for reasons of better understanding.

First, to round it off and for a better understanding briefly on some basic relationships in a z. part also repeat statements of the parent application Received meaning. The invention is based on the fact that for the formation of the injection time (or another Specification of the to be fed to an internal combustion engine Amount of fuel when used in carburetors or the like.) in normal operation (usually start conditions, Emergency running, overrun cut-off and Like not included) is based on an injection time map is dependent on the speed throttle valve position can be and for example via a predetermined  Number of speed and throttle valve signal Support points is spanned. Can in numerical values for example 15 speed and 15 throttle valves Signal support points may be provided. This basic injection Map can then, for example, on a special Vehicle designed for each vehicle type be. For example, to adapt to other vehicles with deviations in the engine area, for example, with throttle valve supports u. The like. This basic map becomes a structural one Map superimposed on that, in numerical values expressed, for example 8 speed and 8 throttle signal Support points can have. These pose a subset from the 15 × 15 sampling points of the injection time Map.

For the adaptation of boundary conditions, which are multiplicative affect overall mixture formation (come here for example environmental pressure differences due to altitude, Temperature, aging of aggregates or the like in question), a so-called global factor also serves.

The respective adaptation area for the learning process, here first the structural adaptation in the map, is shown in FIG. 2 and results from the agreement that, as already mentioned above, the 8 × 8 support points of the structural adaptation map are a subset of the 15 × 15 support points of the basic map, which can also be referred to as the injection time map, when the fuel is supplied to the internal combustion engine via electromagnetically actuated injection valves. Since every second support point of the 15 × 15 map is a support point of the 8 × 8 structural map, a common base and FSA support point ( FSA = factor for structural adaptation) is surrounded by 8 base points within the map, which is due to the different Network structure (single crossed lines for basic support point and double crossed lines for combined basic and FSA support point) can also be seen from the illustration in FIG. 2. These 8 basic support points span the adaptation surface 10 . When the operating characteristic throttle valve position or angle DK and speed N defining the characteristic diagram enter the adaptation surface at the time t = T 1 , the limited manipulated variable issued by the lambda controller 11 (see FIG. 1) is limited after X ignitions and without taking into account the respective "previous history" divided into global and structural parts. A learning cycle is defined as the time period between the time of entry t = T 1 (entry point) and the time of exit t = T 2 (exit point).

The learning process therefore proceeds as follows. The output of the lambda probe is fed to the lambda controller 11 at its input; At the output of the lambda controller 11 there is a standardized lambda manipulated variable Xr , which, after passing through a limiting block 12 for limiting the actuating stroke with the limit values Xr'max and Xr'min , reaches the low-pass filter 13 as a limited, standardized manipulated variable Xr ' , which is the limited manipulated variable Xr 'of the lambda controller is subjected to averaging. A low-pass output value Ya then results at the output of the low-pass filter, which can include a predetermined (also recursive) low-pass formula, the output value of the low-pass filter Ya at time t = T 1 being set equal to the input value → Ya ( t = T 1 ) = Xr ′ ( t = T 1 ). If then more than X plus a predetermined number, for example approximately 32 ignition pulses, have passed since the entry into the adaptation area 10 according to FIG. 2, then the factor FSA to be learned in the structural area is entered into the characteristic diagram upon exiting the adaptation area (time t = T 2 ) taken over, whereby this factor at the time T 2 is composed of the factor at the time t ≦ ωτ T 1 and the new value of the low pass Ya at the time T 2 , resulting in the following formula:

FSA ( T 2 ) = FSA ( t ≦ ωτ T 1 ) + Ya ( T 2 ).

The term FSA ( t ≦ ωτ T 1 ) is the factor stored for the structural area FSA during the last learning process.

An essential inventive measure in this context is that an adjustment of the factor for the structural area per learning cycle is only permissible by a predetermined percentage value, specifically only 3% in the preferred exemplary embodiment, ie the Δ value of the FSA is relative Coarse gradation only at 0.03 and therefore, as will be explained further below, significantly different from the Δ value of the factor for the global adaptation FGA . Attention is drawn overall to the illustration in FIG. 1, switches or function blocks which have not been explained so far being specified further below in connection with supplementary aspects in the present invention.

The learning process, that is to say the formation of the factor for the global adaptation FGA, takes place in the same way as with the structural adaptation within a learning cycle (time between T 1 and T 2 in FIG. 2) and in approximately the same way, with only the limited and filtered manipulated variable Ya of the lambda controller 11 can be filtered again with a corresponding filter formula.

The factor FGA to be learned is also formed when exiting the adaptation surface ( t = T 2 )

FGA ( T 2 ) = FGA ( T ≦ ωτ T 1 ) + Zg ( T 2 ),

where Zg ( T 2 ) is the value of the lambda controller manipulated variable present at the time T 2 at the output of the second filter.

Global as well as structural adaptation are prohibited at the operating states Lambda control switched off, with warm-up enrichment, start u. for an adaptation of operating conditions that cannot be evaluated the internal combustion engine.

In contrast to the permitted adjustment of the factor of the structural adaptation FSA of, for example, 3% per learning cycle, a much finer gradation is permitted for the global factor, for example only 1/8 or 1/16 of the resolution of the structural factor FSA , like the two grading blocks 14 a and 14 b in Fig. 1 also specify. This different gradation is intended to ensure that given a stable structure in the internal combustion engine area (for example, throttle valve manifold does not change, internal combustion engine is stable), however, there are changing multiplicative, that is to say global influences which cause a change, such as gradual changes in air pressure and the like. Like., These are not communicated to the structural, but only the global factor. By making the grading for possible changes in the structural factor FSA much coarser, for example with the specified 3%, than the changes in the global factor (grading e.g. 0.19%), slow changes with only a slow rate of change (this remains then probably less than 3% per learning cycle) will always be compensated for by the global factor, i.e. it must be absorbed before the change threshold (3%) for the structural factor FSA is reached. A trip over a passport height can be assumed as an example, in which the air density changes slowly with increasing altitude and thus an adaptive compensation is effected via the global factor FGA .

Larger expected changes can occur, however, if, for example, a vehicle also turned off extreme low pressure weather conditions and at a corresponding high pressure weather situation started again becomes.

To compensate for this effect, which also has a multiplicative effect, but which can then suddenly be greater than, for example, the specified 3%, according to an advantageous measure of the present invention, only global adaptation via the global factor FGA is allowed after start for a limited number of learning cycles. One can therefore also see in the illustration in FIG. 1 a switch 15 to be understood here as an example, which disconnects the connection to the low-pass filter and thus forms a structural ban. Only when the multiplicative deviation has been compensated is the adaptive compensation permitted if the structure changes. This results in a considerably improved stability of the learning map and avoids unnecessary "breathing", also with the advantage that support points positions in the map area (structure) that can be evaluated perfectly for any subsequent shutdown of the internal combustion engine are available for subsequent use.

Accordingly, the total injection time t _i output during normal operation is made up as follows:

t _i = FSA · FGA · π _i Fi · t _L ′ + t _{- S.}

In this formula, FSA and FGA are the factors for structural adaptation (from 8 α · 8 N map with α = throttle valve angle and N = speed) and for global adaptation, π _i Fi are factors from other functions for the formation of t _i , t _L 'is the basic injection time from the 15 α · 15 N map and t _S is the valve delay time.

In the structural map, when the factors are output not interpolated. To jump at the transition from one base to the next the manipulated variable of the lambda control in opposite percentage  changed by the same amount that the distinguish between the factors stored at the two support points.

A further embodiment of the present invention is based on the knowledge that, if the learning process assumes convergence per learning step (learning cycle), the full deviation is adopted as a correction value in the learning factors. The deviation is divided so that over the low pass 13 in the parallel branch for structure and global adaptation downstream weighting elements 16 a , 16 b , that the greater part of the deviation is added to the structural factor FSA and the smaller part to the global factor FGA . The weaker weighting ( k ) of the global factor means that structural corrections, if (inevitably) they are also taken up by the global factor, do not radiate too strongly to the other map points (structure). The different distribution of the correction value caused by the weighting blocks 16 a , 16 b therefore ensures the tendency to adaptively make structural corrections in the factor for the structural correction (and therefore only in a certain map area) and to affect the entire map in the same way (multiplicative ) to record changes that have an impact via the global factor. This tendency is further strengthened and supported by the different grading mentioned above (Δ values for FSA and FGA per learning cycle), based on the assumption that global changes are relativized and suppressed by the rough grading in the ability of the structural factor to be adopted.

The circuit breaker 17 provided in FIG. 1 at the output of the low pass 13 serves to implement the general prohibition on adaption.

A further, particularly advantageous embodiment of the invention consists in dynamically improving the adaptation of the mixture control, specifically in that, for example, the averaging (via the low-pass filter 13 ) of the manipulated variable X'r of the lambda controller 11 , the formation of structural and global Factors FSA and FGA, as well as, for example, the counting of the settling delay and the minimum averaging time in the grid of the steps of the manipulated variable of the lambda controller (see FIG. 3), the number of these passes being the number of probe passes which is an operating parameter (actual value) Internal combustion engine is, corresponds.

Compared to a previously used time grid, there is therefore the advantageous adaptation of the adaptation dynamically to the operating parameter λ , in other words, the adaptation does not run asynchronously in a time grid. The respective factors can be optimally determined by the dynamic adaptation - this also results in a self-locking design of the adaptation, because if the controller runs against one of its actuating stops, no further adaptation takes place because the manipulated variable no longer performs jumps and in this case is implausible is seen.

In this variant of the present invention, the bans for structural and global adaptation are retained with basically the same formation of the factors and the additional ban for structural adaptation after a start is defined for a predeterminable number of x learning cycles of the global factor FGA . During these x learning cycles, opening the switch 15 results in a structural ban - the weighting of the filter output Ya is also retained for the global factor for these learning cycles.

After the x learning cycles have elapsed, adaptation takes place in parallel, globally and structurally according to the weighting k and ( k -1). However, the limitation to the 3% correction per FSA learning cycle mentioned earlier can be omitted, so that any multiple of 3% per learning cycle can be changed in the structural factor FSA - the change levels for the global factor FGA always remain 8 times or 16 times finer.

It is essential that, as can be seen from the diagram in FIG. 3, the manipulated variable of the lambda controller 11 with permitted adaptation (structure and / or global) is each processed with the value in the low-pass filter that results after the manipulated variable has jumped , ie at the jump times t = i ; i + 1; i + 2.. . . After the expiration of n _{s of} such jumps in the manipulated variable, which follow an adaptation area change, the low-pass filter 13 is then set

Ya ( i + n _s ) = Xr ′ ( i + n _s ).

In other words, the low-pass filter 13 is released (set to the initial value) by the number of jumps that occur from the time a new adaptation surface is entered, the further values then being determined in accordance with the recursive low-pass formula given below:

The recursion takes place after each manipulated variable jump. The adoption of the correction value for the adaptation factor (formation of FSA and FGA ) determined with the above equations is only permitted when at least m _s recursion steps of the filter 13 have been completed.

If the conditions n ≧ n _s and m ≧ m _s are then fulfilled at the time t = T 2 (adaptation area change), then the current filter output value Ya ( j ) via the weighting levels k and (1- k ) and corresponding quantization at 14 a and 14 b further processed as a correction value for the new formation of the factors FSA and FGA , as the block diagram of FIG. 1 shows. This also explains the switches in FIG. 1, namely a first switch 18 , which closes when the condition n ≧ n _s is met and acts on the low-pass filter 13 with the output of the limiting block (limited manipulated variable of the lambda controller) - and of the switch 19 which forwards the output value of the filter for parallel processing and adaptation when the condition m ≧ m _{s is} met.

At the time of an adaptation area change, n and m are set to zero; while FIG. 3, it is assumed that n _s = 4 in the diagram of the course.

The following table shows the adjustable sizes as well as their range limits which are recognized as advantageous - it is not to be regarded as restrictive.

By stopping (counting) probe passes or The adaptation becomes jumps in the manipulated variable of the lambda controller automatically self-locking when the controller is connected to a his positioning stops is running. It makes sense because of of the large selectable adjustment and adaptation range To assess the case as implausible and, at least as first possibility, then no longer to adapt.

All specified in the description and in the claims new features can be essential to the invention alone or in combination be.

Claims (11)

1.Method for adapting the mixture control in internal combustion engines, a characteristic map spanned by operating variables (throttle valve position angle DK , rotational speed N ) of the internal combustion engine producing a pilot control variable which is decisive for the quantity of fuel to be supplied or injected and which is adjusted by at least one adaptively variable correction variable (structural adaptation, global adaptation) is influenced, characterized in that the correction of the structural factor adjustment (Δ value of FSA ) is limited to a predetermined value (3%) per FSA learning cycle, with the requirement of a multiple (1/8 or 1/16 ) finer gradation (0.19%) of the adaptation correction of the global factor ( FGA ) such that, before reaching the change threshold for the structural factor ( FSA ), slow changes due to environmental influences with only a slow rate of change (≦ λτ3% per learning cycle) via the global factor ( FGA ) can be compensated. 2.Method for adapting the mixture control in internal combustion engines, a characteristic map spanned by operating variables (throttle valve position angle DK , rotational speed N ) of the internal combustion engine producing a pilot control variable which is decisive for the quantity of fuel to be supplied or injected and which is adjusted by at least one adaptively variable correction variable (structural adaptation, global adaptation) is influenced, characterized in that the adaptation (structural factor FSA and / or global factor FGA ) is dynamically adapted to the actual operating parameter ( λ ) by the formation of structural and global factor ( FSA, FGA ), further preferably the message the manipulated variable of the lambda controller, counting of the settling delay u. The like. In the grid of the jumps of the manipulated variable ( Xr ) of the lambda controller ( 11 ) takes place and is related thereto, the manipulated variable jumps of the lambda controller corresponding to the number of probe passes of the lambda probe. 3. The method according to claim 1 or 2, characterized in that for a predetermined number ( x ) of learning cycles (change of the adaptation area) the structural adaptation does not take place after a start (structure ban). 4. The method according to any one of claims 1-3, characterized in that the full deviation as a correction value in the adaptive learning factors ( FGA, FSA ) are adopted per learning cycle with the proviso that the deviation is divided so that the greater part of the structural factor ( FSA ) and the smaller part is added to the global factor ( FGA ) (different weighting of the low-pass output value ( Ya ) recorded as a correction value when leaving the adaptation area). 5. The method according to claim 2, characterized in that the manipulated variable of the lambda controller ( 11 ) with permitted adaptation (structure and / or global) is each processed with the output value ( Ya ) of the low-pass filter ( 13 ), which changes after the jump the manipulated variable ( Xr ′ ) results. 6. The method according to any one of claims 2 to 5, characterized in that after a predetermined number ( n _s ) of manipulated variable jumps that have followed an adaptation area change, the initial value ( Ya ( i + n _s )) of the low pass ( 13 ) set to a predetermined value ( Xr ′ ( i + n _s )) and the other values are determined in accordance with a recursive low-pass formula, the recursion taking place after each step in the manipulated variable, but the adoption of the correction value issued by the low-pass filter for the respective adaptation factor ( FSA, FGA ) takes place only after a predetermined number (m _s) is completed by recursion steps of the filter (13). 7. The method according to claim 6, characterized in that the values of the low pass according to the recursive low pass formula be determined. 8.Means for adapting the mixture control in internal combustion engines, a map spanned by operating variables (throttle valve position angle DK , rotational speed N ) of the internal combustion engine producing a pilot control variable which is decisive for the quantity of fuel to be supplied or injected, which is determined by at least one adaptively variable correction variable (structural adaptation, global adaptation) is influenced to carry out the method according to claim 1, characterized in that step function blocks ( 14 a , 14 b ) are provided which only allow the averaged manipulated variable value to be adopted as a correction variable for adaptive factor change if the change has a predetermined Δ value ( Δ - FSA = 3%; Δ - FGA = 0.19%) with the possibility of limitation to this Δ value per learning cycle. 9.Means for adapting the mixture control in internal combustion engines, a map spanned by operating variables (throttle valve position angle DK , rotational speed N ) of the internal combustion engine producing a pilot control variable which is decisive for the quantity of fuel to be supplied or injected and which is adjusted by at least one adaptively variable correction variable (structural adaptation, global adaptation) is influenced to carry out the method according to claim 2, characterized in that counting and switching means ( 18, 19 ) are provided which, with dynamic adaptation of the adaptation to an actual operating parameter ( λ ), the forwarding of the manipulated variable ( Xr or Xr ' ) For the averaging low-pass filter ( 13 ) only when a predetermined number ( n _s ) of manipulated variable jumps or probe passes has elapsed. 10. The device according to claim 9, characterized in that the acquisition of the determined by the low-pass filter correction value (Ya) for adaptation factor (FSA, FGA) is only released by counters and switch means (19) when a predetermined number (m _s) of recursion steps of the manipulated variable of the lambda controller ( 11 ) averaging filter ( 13 ) is completed. 11. Device according to one of claims 8-10, characterized in that weighting stages ( 16 a , 16 b ) are provided which different from the low-pass filter ( 13 ) correction variable ( Ya ) (weighting k or ( k -1) the Apply change grading blocks ( 14 a , 14 b ) for structural factor ( FSA ) and global factor ( FGA ).