WO1998037321A9 - Procede et dispositif de regulation du melange dans un moteur a combustion interne - Google Patents

Procede et dispositif de regulation du melange dans un moteur a combustion interne

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Publication number
WO1998037321A9
WO1998037321A9 PCT/EP1998/001001 EP9801001W WO9837321A9 WO 1998037321 A9 WO1998037321 A9 WO 1998037321A9 EP 9801001 W EP9801001 W EP 9801001W WO 9837321 A9 WO9837321 A9 WO 9837321A9
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WO
WIPO (PCT)
Prior art keywords
input
internal combustion
combustion engine
variable
fuel
Prior art date
Application number
PCT/EP1998/001001
Other languages
German (de)
English (en)
Other versions
WO1998037321A1 (fr
Filing date
Publication date
Priority claimed from DE19706750A external-priority patent/DE19706750A1/de
Application filed filed Critical
Priority to EP98913572A priority Critical patent/EP0966600B1/fr
Priority to DE59808282T priority patent/DE59808282D1/de
Publication of WO1998037321A1 publication Critical patent/WO1998037321A1/fr
Publication of WO1998037321A9 publication Critical patent/WO1998037321A9/fr

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Definitions

  • the invention relates to a method for mixture control in an internal combustion engine and to a device for carrying it out
  • the mixture control not only plays an important role in the operating behavior of an internal combustion engine, but is crucial for the achievement of lower emissions of harmful exhaust gases So überbowt the usual in gasoline engines
  • the mixture is generally adjusted by pilot control and superimposed control.
  • the remaining oxygen content in the exhaust gas is measured via a lambda probe arranged in the exhaust gas flow.
  • a deviation from ⁇ -1 results in a greater or lesser residual oxygen content.
  • the lambda probe generates a corresponding output signal.
  • the characteristic maps are determined by the engine manufacturer in a comprehensive test on test states stored in the map to compensate for the series deviation of the decisive for the combustion engine parameters
  • the mixture control uses the value specified in the map value for the injection due to the several working cycles amounting dead time (namely until a certain injection quantity in the exhaust gas at the location of the lambda probe noticeable), it is not possible for this regulation to maintain the desired mixing ratio after load or speed changes. In practice, this is the reason for acceleration and deceleration phases the lambda control is completely inactivated, the injection quantity is then controlled only with the aid of the characteristic map. In dynamic driving, therefore, there are considerable deviations from the lambda nominal value, which leads to correspondingly high pollutant emissions. Strict exhaust gas regulations can not be met or only with difficulty
  • the present invention teaches a novel Artificial Intelligence method that provides a "direct" approach to regulation by providing the method
  • Tax law "learns"
  • the invention has the object to provide a method with which the above disadvantages are avoided or at least reduced, so that even strict emission standards can be met. This also includes the provision of a corresponding device
  • the term "measuring” is understood in a broad sense, which includes the actual physical measurement and, if necessary, the derivation of a variable thereof.
  • the measured variable can therefore be an immediately measured magnitude or a quantity derived therefrom.
  • the measured value sensor for the actual quantity can be a lambda probe , which emits a signal corresponding to the residual oxygen content of the exhaust gas.
  • “measuring” can, in addition to obtaining this signal, also determine the residual oxygen content and, if appropriate, determine the Lambda values include
  • the mixture quantity of interest ia is the mixture ratio (ie the lambda value) It is possible to directly measure a mixture size or make a measurement of an exhaust gas quantity that allows a conclusion on the mixture size of interest (eg the lambda value)
  • This mixture ratio ⁇ is defined as the ratio of air mass ( m ac i) and fuel mass (m fcyl ) in the engine cylinder (combustion chamber),
  • the task of the engine control system is therefore to determine the mixture ratio as a function of
  • the direct injection is known for diesel engines This also includes diesel engines with vortex or vortex chamber, if they are effectively part of the combustion chamber.
  • Direct fuel injection for gasoline engines was already mass production more than 50 years ago, Daimler Benz military aircraft engines were with a mechanical Equipped with direct injection After the Second World War mechanical direct injection was used in the Mercedes 300 Sports Car and Lloyd small car, but in the end the mechanical outlay was unprofitable In view of increasing environmental protection requirements, such engines, which are expected to generate low fuel consumption, are up to date again
  • the mixture ratio ⁇ in the piston internal combustion engine is determined by the air and the fuel mass in the cylinder Therefore, consideration of the mixture formation is favorable as the intersection of two paths, the air and the fuel path
  • the intake behavior, the so-called air path to be described The engine sucks in open intake valves and running down the piston fresh air from the intake manifold.
  • the so-called choked-flow effect the inflow velocity of the air through the intake valves into the cylinders can reach maximum speed of sound.
  • the cylinder thus contains less air mass than would be possible according to its volume in the thermodynamic Saugrohrschreib.
  • the suction behavior of the engine can be described mathematically as a pump with non-linear efficiency, the air mass (m t ) that has entered the cylinder per intake stroke results
  • T m temperature of the air mass
  • p m pressure of the air mass
  • V D stroke volume of a cylinder
  • volumemetric efficiency ⁇ vol is non-linearly dependent on the intake manifold pressure and the speed of the engine (which determines the duration of the intake stroke) in engines with throttle valve (gasoline engine, conventional, suction or turbo), ie
  • the load control happens z B on the duty cycle a a of the intake valve, so the volumetric efficiency is determined by just this duty cycle and the speed, ie
  • the air mass in the cylinder after the intake stroke is also determined by the pressure and the temperature in the intake manifold. If the engine is not equipped with a throttle, these thermodynamic conditions correspond to those of the environment, it can be assumed that these are only slow vary, with an adaptation can be dispensed with the measurement of both large If the internal combustion engine, however throttled operated, both large variables are rapidly variable, then z B also pressure and possibly temperature in the intake manifold can be measured to determine the air mass in the cylinder the air mass sucked into the combustion chamber per working cycle thus results in the non-linear dependency
  • Camshaft Steuronne also occurs the dependence of the volumetric efficiency of these control variables
  • the mixture ratio (the air ratio ⁇ ) is thus determined to be
  • Fig. L this relationship is exemplified for a piston internal combustion engine with conventional intake valve control, and although Fig. 1 shows a "Signalflußplan" for the formation of the mixture ratio in direct injection piston internal combustion engines with conventional intake valve control
  • p m «p 0 (/? 0 ambient pressure) or for a throttled Otto engine p m ⁇ p 0
  • the intake manifold pressure may also exceed the ambient air pressure.
  • the volumetric efficiency z B depends on the duty cycle a a and the engine speed n can then be done by varying the duty cycle, the engine sucks unthrottled
  • the Gemischverhaltnis is then formed, for example, according to the illustration of Figure 2, which shows a "Signalflußplan" for the formation of the mixture ratio in direct injection internal combustion piston engine with camshaft inlet valve control
  • Both and NL j are here static (possibly time-variant) nonlinearities and determinable only by a complex calibration
  • the mixture ratio can be measured, novel methods are based on the interpretation of the ionization current in the electric spark ignition (gasoline engine), but conventionally, the measurement by means of faster switching Lambda probe
  • mapping given for inventive methods is from the mass air mass (input size) to the size of the fuel
  • input size the mass air mass
  • direct-injection engines the method according to the invention can also be applied to non-direct-injection engines which have a so-called "direct injection” engine. Have fuel storage effect.
  • mapping is not - as in the prior art - mediated by a control, but by a learning control
  • the learning can be done during operation, in which the various occurring in practice operating points (such may, for The method according to the invention is carried out in each relevant, ie achievable, operating point. After all the operating points have been run through several times-which is generally achieved relatively quickly in normal operation-the overall figure for all possible values is .PHI Input size learned
  • the controller then delivers instantaneously - ie without any control delay - the correct value of the output variable with high accuracy, even after a change of the operating point.
  • the learning process continues to be carried out in order to enable an ongoing adaptation to disturbance variables These may be, for example, wear-related changes, changes in the intake air temperature, the coolant water temperature, the external air pressure, the oxygen content of the air, etc.
  • the inventive method is able to ensure the start of new operating points without any ⁇ -deviation after learning the image conventional control approaches always occurs in contrast to a control difference, which is then integrated, for example, by an integrating portion in the controller as long as a control until the difference has become zero
  • the invention thus has the following advantages. Due to the self-adaptation of the image information, the accuracy requirements with which the image information for the image is reduced are reduced This reduces the effort for the development of a motor control and series development considerably, the control method according to the invention is robust with respect to series variations and time-varying disturbances, the desired lambda value is not only in steady-state operation, but also after a change of operating state (rotational speed). and / or load changes of
  • the invention thus contributes to the protection of the environment and the careful use of limited resources
  • the invention can also be used in the course of a fault diagnosis during operation. If the degree of adaptation required exceeds that of conventional series scattering and interference, let it be concluded that the condition is inaccurate, such as inadmissibly high wear or a defect due to appropriate evaluation of the degree of adaptation , for example, by a vehicle diagnostic system, engine failure or partial failure early -i ⁇ g recognize during operation
  • the method can be carried out such that one or more of the method steps a) -f) are carried out averaged over several cycles or cycles of a cylinder.
  • the method is in time with the Working cycles of the individual cylinders carried out (claim 2), ie, the sequence of process steps a) -f) is carried out once in the context of a single stroke of a single cylinder
  • the measurement in step a) takes place during the intake stroke of a cylinder in step b )
  • the amount of fuel to be supplied on the basis of this measurement (and possibly previous measurements, more details below) is determined
  • the supply of fuel in Schntt c) is then z B in the Immediately following compression stroke of the same cylinder, ie in the same cycle as step
  • the most common gasoline engine is a throttled engine, which is usually controlled by adjusting a throttle valve disposed in front of a suction pipe in such a motor, the input size or - for several input variables - one of the input variables advantageously the pressure in the intake manifold (claim 3) determines this pressure
  • the cylinder filling may be an engine with or without turbocharging.
  • the pressure in the intake manifold may be temporarily or permanently above atmospheric pressure
  • the intake system is designed to adapt the dynamic charging various Betnebsbedmgonne (see Automotive Handbook, Bosch, 1991, S 373, "switching intake systems")
  • the effective Saugrohrlange can be adjusted to use acoustic phenomena to increase the filling (keyword resonance charging)
  • z B the effective Saugrohrlange
  • the input size or one of the input variables claim 4
  • valve timing parameters eg Camshaft rotation and / or axial displacement
  • input size or one of the input variables claim 5
  • the duty cycle and / or the closing and / or opening time of the intake valve is an input size or one of the input variables (claim 6).
  • the duty cycle is related to the duration of the intake cycle or the total cycle Of ⁇ hungsdauer
  • the inventive method is suitable with a simple supplement for the latter Betneb260, and although in the target mixture ratio the role of an input variable is assigned (claim 8) While in an Otto engine, the input large space z B is two-dimensional (it is clamped about by intake manifold pressure and speed), comes in this embodiment for a diesel engine as For example, third input variable adds the desired mixture ratio, so that here a three-dimensional input large space is clamped (for example by intake manifold pressure, rotational speed and target mixture ratio).
  • the fuel supply by injecting
  • the output quantity controlling the amount of fuel to be supplied then one or more of the following Great Einspntzdauer, Tastverhaltnis Einspritzventiloffhung, injection pressure, degree of opening of the injection valve (claim 9)
  • the duty cycle is also the Of ⁇ hungsdauer related to the Duration of a work cycle or a working cycle
  • the degree of opening of the Einspntzventils can be controlled z B on the stroke of the valve needle
  • the fuel injection is not carried out directly in the cylinder, but in the suction pipe upstream them
  • the fuel wets here first the suction tube wall (so-called wall-wetting effect) and then required for the transition to the gaseous phase a certain time.
  • the method according to the invention can also be used advantageously in internal combustion engines with a fuel storage effect.
  • the amount of fuel injected into a cylinder at a particular power stroke generally depends on the quantity of fuel injected during preceding cycles.
  • large quantities of one or more preceding power strokes are also possible
  • the determination of the output variable (s) in step b) may be involved. This may be, for example, the fuel quantities injected in these preceding cycles, which still have an effect in the current cycle. Their consideration allows a very precise control of the injection rate in the current cycle Kraftstof ⁇ menge
  • the stored image information is in the form of a map containing the output size directly or indirectly.
  • a map is obtained, for example, by discretizing the (generally multi-dimensional) input size space and providing each input size cell formed by the discretization an output size Assignment of several output variables
  • the discretization of the input space does not have to be regular, likewise the size of the input cells must not be constant. Learning takes place in such a way that in step f) the stored values of one or more adjacent input cells corresponding to a detected deviation be adjusted that a smaller deviation occurs in a future operation in the same input large cell
  • mapping of the input size / s to the output size / s and the adaptation of the stored image information by a neural network algorithm (claim 14). Due to the parallelization of the data processing in so-called neurons forming the network, the use of a neural network - Algorithm the calculation time of the output size based on the stored image information compared to other interpolation methods (eg splines, linear or polynomial interpolation) significantly shortened using matched hardware While using known interpolation already by selecting the interpolation (possibly nonexistent Given that prior knowledge of the relationship to be approximated is assumed, a neural network can do without any prior knowledge. In particular, neural networks are suitable for the present adaptive method due to their evaluation and adaptation algorithm can be used very advantageously
  • a neural network NN is set so as to map the control path of the air path (eg, intake manifold pressure) to the control path of the fuel path so as to achieve a desired mixture ratio.
  • the control path of the air path is also considered from a historical point of view considered the control size, with which the driver affects the power output of the internal combustion engine
  • the adaptive neural network NN is intended depending on the driver-influenced control size for the air path and possibly from Engine operating state (eg speed) the control size, here z B ⁇ y for the
  • Output fuel path The output of the neural network thus corresponds to the duty cycle of the injection valves a f , the input space of the neural network NN consists in this example of intake manifold pressure p m and engine speed n, so we write
  • the aim of the control is to have a desired air-fuel ratio for each operating point
  • the output quantity is advantageous in this case by linking a the stored mapping information representing Stutz value vector and one of the / the A large gear ⁇ / n dependent activation vector formed (claim 15)
  • This linkage is preferably linear, and has in particular the form of a dot product or - in the case of multidimensional output size - of a vector Matnx product (claim 16). According to the example above, one then obtains the control quantity a f
  • the activation vector is normalized and depends only on the distance of the input size / s to the support points on which the vector representation is based (claim 17).
  • the standardization condition is z B
  • the dependence of the activation vector A only on the distance of the input variable (s) to the support points can be expressed by the fact that its components A t depend only on a variable which corresponds to the distance to the component belonging to the considered support point
  • ⁇ t are the bottlenecks, ie the locations of the neurons in the input space
  • mapping of the input variable (s) to the output variable (s) is substantially local (claim 18). This means that for a given value of the input variable (s) substantially only the truncation value e contribute to the mapping to the output size (s) immediately adjacent to the input size (s). This is advantageously accomplished by having only the n component (s) of the activation vector obtains appreciably large values at close range to the input / s lie / lie, while components are negligibly small or disappear at a greater distance (claim 19)
  • the components of the activation vector depend on the distance of the input variable (s) to the associated nozzle location according to a center function (claim 20), examples of advantageous center functions
  • the "range” of learning does not have to match that of the figure.
  • both ranges are chosen to be substantially the same, ie the adaptation of the image information takes place essentially in the same range of the distance from a deviation point, which is also reflected in the image of a is included at this point input size to the output size (claim 21)
  • the adaptation of the image information is carried out substantially locally to the deviation point (claim 22). This is let through, for example
  • ⁇ is a trimming value correction vector.
  • both the adaptation and the image occur locally and with the same range
  • the adaptation of the image information is preferably carried out by adding to the bleed value vector a bleed correction vector which is proportional to a combination of the deviation value with the activation vector.
  • the linkage is in particular the product of the deviation value with the activation vector Adaptation eg according to
  • the factor ⁇ in this equation represents the learning step size.
  • the large e is the measured mixture error at the operating point that corresponds to
  • Convergence is the convergence against a global minimum of the distance between ⁇ and ⁇ thus understood in all operating points approached for learning (not just convergence against a local minimum). Convergence then means that the learning process can only be completed when the trimming value vector opposes the Converged Only Possible, But Unknown Trimming Value Vector Reference is made to the following description of a proof of stability and convergence of a preferred law of adaptation.
  • a device for controlling mixture in an internal combustion engine comprising: a) at least one device for measuring at least one quantity with which the air mass entering a combustion chamber of the internal combustion engine is related (so-called input variable b) at least one adjusting device for supplying fuel, c) at least one device for measuring a quantity, which carries information about the resulting mixture or its combustion (so-called actual size); d) at least one memory for receiving the variable
  • Fig. 1 is a signal flow diagram for the formation of the Gemischverhaltnisses in a direct-injection piston internal combustion engine with conventional intake control;
  • FIG. 2 is a signal flow chart corresponding to FIG. 1, but for an unthrottled internal combustion engine with freely actuable intake valves,
  • FIG. 3 is a schematic representation of a direct injection gasoline engine with conventional intake valve control
  • FIG. 4 is a signal flow diagram corresponding to FIG. 1, but showing a neural network algorithm
  • 5 shows a so-called parallel representation of a neural network algorithm
  • FIG. 6 shows a signal flow diagram of a neural network algorithm with an input dimension
  • FIG. 7 shows diagrams representing the network output as a function of a one-dimensional input value, for illustrating the locality of the sample value adaptation
  • FIG. 8 shows an illustration of the interpolation and extrapolation behavior of a neural network algorithm
  • FIG. 10 is an illustration of an exemplary non-linearity for the air path
  • FIG. 11 is a fuel path diagram corresponding to FIG. 10
  • FIG. 12 is a graph of the time course of the output to start 13 is a diagram according to FIG. 12, but in the course of learning,
  • FIG. 14 is a diagram according to FIG. 12, but after the learning process is almost completed.
  • the question is whether learning always leads to the correct result without wrong paths, a mathematical proof of stability and convergence should be feasible.
  • the exemplary procedure to be described below becomes a special neural network
  • the pulse duty cycle of the injection by direct injection (solenoid) valves as a control size is expediently the neural network next fixed stationary prior knowledge recognized as a correction element, which is adopted online so that always the desired Gemischverhaltnis is adapted.
  • the load control in this internal combustion engine type is done via the position angle of the throttle, the driver controls the air mass flow m at in the intake manifold with open intake valves of the cylinder sucks during the downward movement of the piston in the intake stroke the air mass flow m m until after closing the fresh air mass m, in the cylinder is available for combustion
  • the fresh air mass in the cylinder depends decisively on the thermodynamic Saugrohrschreib pressure p m according to equation 2
  • the driver so determined via the throttle position, the air pressure in the intake manifold and thus indirectly the load in the cylinder
  • the engine speed determines the time available for intake, so that a rule of thumb is that at lower engine speed, more fresh air can be sucked in the same thermodynamic state as in the intake manifold
  • suctioned air mass m in this example depends continuously nonlinear from the intake manifold pressure and speed, with variable Ansauggeomtrie also of the control angle or the adjustable intake camshaft Mathematically we write so
  • mixture control now consists in setting up a neural network NN so that the control path of the air path (here the intake manifold pressure) is mapped to the control amount of the fuel path so as to achieve a desired mixture ratio
  • the adaptive neural network ⁇ W should output the control variable (here a ⁇ ) for the fuel path depending on the engine operating state (in this case the rotational speed) and the driver-influenced control variable for the air path (here the intake manifold pressure).
  • the output of the neural network thus corresponds here to the duty cycle of the injection valves a f , the input space of the neural network ⁇ W consists of
  • the aim of the control is to have a desired air-fuel ratio for each operating point
  • NL a (P m , n) ⁇ ⁇ NL j [NN (/, "" //), "] (28)
  • THANN ⁇ ⁇ NL j [NN (/, "" //), "]
  • Ae (p m , n) A ⁇ (p m , n) a f p m >)
  • the sign of the error change is thus always inverse to the sign of the error, in other words, if the error is less than zero (e ⁇ 0), its value increases ( ⁇ e> 0), its magnitude becomes smaller its value falls, so here too the amount is smaller Thus, the error can only converge to zero
  • DNA is explained in more detail as an example of a suitable adaptive neural network algorithm. It combines the advantages of normalized RBF networks with the advantage of computing time and memory optimization
  • the extrapolation behavior of the THEN at points at which no knowledge in the form of learned parameters is available, is defined as well as meaningful: the output of the network corresponds to a (weighted) average of the learned knowledge in the learned environment; the THEN differs significantly from the original RBF network.
  • the network consists of locally activated neurons, ie mainly the neurons in the immediate vicinity of the network input x are activated.
  • the structure of the THEN is subdividable into activation and weighting. This illustrates the signal flow diagram representation of a THEN shown in Figure 6 with an input dimension (ie, scalar input x), A (x), and 9; are
  • Equation 44 guarantees the boundedness of activation A x), which becomes normalization
  • This figure shows the interpolation and extrapolation behavior of THEN, the crosses denote the existing data points Im learned area, the curve approximated by the truncated values (crosses) agrees with the sine function to be learned beyond the learned range, the estimated value tends to increase with increasing distance from the nearest truncated value to the average of all existing knowledge (truncation values) to the stored knowledge (data points) will result in the mean of the existing knowledge, in the immediate vicinity of a data point determines this mainly the network output If the nozzle values in their weight ⁇ , defined as adjustable, then the simplest, shown in Figure 9 online structure to This representation applies to continuous-time systems The THEN shown can learn all static (without internal states such as memory) non-nite-data to group the activation method apart from a small approximation error due to the fi
  • the illustrated learning ability of the THEN is used to synthesize a fuel injection control law by making the error between the target mixture ratio and the actual mixture ratio zero during learning
  • FIGS. 12, 13 and 14 show the control curve according to equation 52 and the online learned from the network course of a / during the learning process These figures show the optimal course of the control size (dashed) and the network learned course (solid) Fig 12 shows the beginning of learning (which was started without any prior knowledge FIG. 13 shows the control curve course during learning, and in FIG. 14 the learning is virtually completed.
  • the mixture error, which rapidly becomes smaller during learning, is shown in FIG.
  • the figure shows the relationship to be learned and the learned context, in each case the control surface.
  • the target context only the area above the input space to be traversed during learning is represented by a simple

Abstract

Le procédé décrit de régulation du mélange dans un moteur à combustion interne comprend les étapes suivantes: (a) au moins une valeur en relation avec le volume d'air qui parvient à la chambre de combustion du moteur à combustion interne est mesurée (valeur dite d'entrée); (b) au moins une valeur de sortie qui règle la quantité de carburant à amener est déterminée en fonction d'au moins la ou les valeurs d'entrée mesurées sous (a), au moyen d'informations enregistrées de représentation; (c) la quantité de carburant déterminée en fonction de la valeur de sortie sous (b) est amenée; (d) une valeur qui comporte des informations sur le mélange ainsi obtenu est mesurée (valeur dite réelle); (e) l'écartement entre la valeur réelle mesurée sous (d) et une valeur nominale est déterminé; (f) les informations enregistrées de représentation sont modifiées en fonction de l'écartement déterminé sous (e) de l'état de fonctionnement mesuré sous (a), afin de réduire l'écartement lorsque les étapes (a) à (e) seront mises en oeuvre ultérieurement pendant le même état de fonctionnement. Ce procédé est réalisé par un processus d'apprentissage. L'invention concerne également un dispositif de mise en oeuvre de ce procédé.
PCT/EP1998/001001 1997-02-20 1998-02-20 Procede et dispositif de regulation du melange dans un moteur a combustion interne WO1998037321A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP98913572A EP0966600B1 (fr) 1997-02-20 1998-02-20 Procede et dispositif de regulation du melange dans un moteur a combustion interne
DE59808282T DE59808282D1 (de) 1997-02-20 1998-02-20 Verfahren zur gemischsteuerung bei einem verbrennungsmotor und vorrichtung zu dessen durchführung

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DE19706750.6 1997-02-20
DE19706750A DE19706750A1 (de) 1997-02-20 1997-02-20 Verfahren zur Gemischsteuerung bei einem Verbrennungsmotor sowie Vorrichtung zu dessen Durchführung

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WO1985003329A1 (fr) * 1984-01-24 1985-08-01 Japan Electronic Control Systems Co., Ltd. Controleur a apprentissage du rapport du melange air/carburant dans un moteur a combustion interne a injection de carburant commande electroniquement
DE3505965A1 (de) * 1985-02-21 1986-08-21 Robert Bosch Gmbh, 7000 Stuttgart Verfahren und einrichtung zur steuerung und regelverfahren fuer die betriebskenngroessen einer brennkraftmaschine
JPH0711256B2 (ja) * 1989-09-06 1995-02-08 本田技研工業株式会社 内燃エンジンの制御装置
SE509805C2 (sv) * 1994-08-11 1999-03-08 Mecel Ab Metod och system för reglering av förbränningsmotorer

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