WO2015144287A1 - Procédé permettant de faire fonctionner un moteur à combustion interne, procédé permettant de déterminer une structure d'apprentissage pour le fonctionnement d'un moteur à combustion interne, appareil de commande d'un moteur à combustion interne, et moteur à combustion interne - Google Patents

Procédé permettant de faire fonctionner un moteur à combustion interne, procédé permettant de déterminer une structure d'apprentissage pour le fonctionnement d'un moteur à combustion interne, appareil de commande d'un moteur à combustion interne, et moteur à combustion interne Download PDF

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Publication number
WO2015144287A1
WO2015144287A1 PCT/EP2015/000508 EP2015000508W WO2015144287A1 WO 2015144287 A1 WO2015144287 A1 WO 2015144287A1 EP 2015000508 W EP2015000508 W EP 2015000508W WO 2015144287 A1 WO2015144287 A1 WO 2015144287A1
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WO
WIPO (PCT)
Prior art keywords
internal combustion
combustion engine
signals
signal
corrected
Prior art date
Application number
PCT/EP2015/000508
Other languages
German (de)
English (en)
Inventor
Jens Niemeyer
Jörg REMELE
Tobias Frank
Original Assignee
Mtu Friedrichshafen Gmbh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Mtu Friedrichshafen Gmbh filed Critical Mtu Friedrichshafen Gmbh
Publication of WO2015144287A1 publication Critical patent/WO2015144287A1/fr

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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/22—Safety or indicating devices for abnormal conditions
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/24—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means
    • F02D41/2406—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means using essentially read only memories
    • F02D41/2425—Particular ways of programming the data
    • F02D41/2429—Methods of calibrating or learning
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/22—Safety or indicating devices for abnormal conditions
    • F02D41/221—Safety or indicating devices for abnormal conditions relating to the failure of actuators or electrically driven elements
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/22—Safety or indicating devices for abnormal conditions
    • F02D41/222—Safety or indicating devices for abnormal conditions relating to the failure of sensors or parameter detection devices
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/24—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means
    • F02D41/2406—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means using essentially read only memories
    • F02D41/2425—Particular ways of programming the data
    • F02D41/2429—Methods of calibrating or learning
    • F02D41/2451—Methods of calibrating or learning characterised by what is learned or calibrated
    • F02D41/2464—Characteristics of actuators
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/24—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means
    • F02D41/2406—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means using essentially read only memories
    • F02D41/2425—Particular ways of programming the data
    • F02D41/2429—Methods of calibrating or learning
    • F02D41/2451—Methods of calibrating or learning characterised by what is learned or calibrated
    • F02D41/2474—Characteristics of sensors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • the invention relates to a method for operating an internal combustion engine according to claim 1, a method for determining a learning structure for the operation of an internal combustion engine according to claim 8, a control device for an internal combustion engine according to claim 15 and an internal combustion engine according to claim 16.
  • Internal combustion engine determines the state of the same using the sensors and sets using the actuators certain operating points. If one looks at a whole series, series or fleet of internal combustion engines, the result is deviations from the ideal behavior of the built-in sensors and actuators scattering by ideally ideal
  • Deviations of the specifically installed sensors and actuators can be detected and could be taken into account in the control of the internal combustion engine. This approach, however, would cause extreme costs, because every single internal combustion engine expensive
  • Test bench time would have to complete.
  • a software-side calibration of the sensors and actuators would be possible before delivery.
  • this is only possible with great effort and also leads to problems when replacing parts in the field of application.
  • a recalibration would be required, which is expensive, time-consuming and associated with high logistical costs.
  • the invention is based on the object, a method for operating a
  • the invention is also based on the object, a method for determining a learning structure for the operation of a
  • the invention has for its object to provide a control device for an internal combustion engine and an internal combustion engine, wherein also said advantages are realized.
  • a plurality of signals is detected, which are generated by at least one sensor and / or sent to at least one actuator. It is basically possible that only signals from sensors or only signals that are sent to actuators are detected. However, both signals generated by sensors and signals sent to actuators are preferably detected. Such signals are also referred to below as sensor signals or measurement signals, if they are from at least one Sensor are generated, and as actuator signals or actuating signals when they are sent to an actuator.
  • the detected signals are evaluated and a specific combination of the signal values of the detected signals is determined. Thus, the signals are not considered individually, but it is determined which specific combination of values of the detected signals is currently present.
  • a correction value for the signal to be corrected is determined.
  • an estimated value for a true value of the signal to be corrected is determined.
  • the signal to be corrected is preferably one of the detected signals. Alternatively or additionally, it is also possible that a signal is corrected which does not belong to the plurality of detected signals.
  • the signal to be corrected may be a
  • Sensor signal or an actuator signal so act to a measurement signal or an actuating signal.
  • the signal to be corrected is corrected with the correction value.
  • the method is based on the idea that the sensor signals and / or actuator signals of the internal combustion engine are faulty, with the concretely installed sensors and actuators showing systematic deviations from their ideal behavior, which however are unknown as such. It turns out, however, that many sensor signals and / or actuator signals of a
  • Internal combustion engine are correlated with each other or contain information about each other.
  • the signals are thus not independent of each other, but linked together for example by physical relationships.
  • statistical information about the systematic deviations of sensors and / or actuators of the installed type are available, can be estimated or even measured on a variety of sensors once. For example, tolerances can be used in data sheets. Such relationships and / or information will be used for a variety of
  • the signals to be corrected are corrected in such a way that, in particular based on a known statistic of a totality of the installed sensors and actuators, the frequency of large deviations and thus the effect of the systematic scattering of sensors and
  • Actuators reduced within the entire fleet.
  • the correction value thus improves in particular the variance of the measuring or positioning errors within a series, series or fleet compared to the uncorrected signal. This makes it possible to comply with stricter requirements, such as stricter emission limits, safety margins can be reduced, and the number of
  • the method is preferably applied not only to a signal to be corrected but to a plurality of signals to be corrected. For this it is possible that a cascading of the method, in particular a cascading of predetermined relationships for different signals, is possible. In this way, preferably several signals are corrected simultaneously. A recursive application of the method is also possible, in which the corrected signal for improving the correction is based on the method again.
  • an analytic function is used as the predetermined relationship.
  • the analytical function describes physical relationships between the detected signals.
  • a redundancy of the signals a correlation of the various signals and / or information contained in the signals is used one above the other.
  • the use of an analytic function to describe such relationships is a very simple implementation with little computational effort. However, it is often found that such
  • a development of the invention provides that, alternatively or additionally, the predetermined relationship is represented by a learning structure, which the correction value in
  • the learning structure preferably describes approximately the same relationships, which in principle should also be reproduced by the analytical function, namely physical relationships between the signals, redundancies of the signals, correlations between the signals and / or
  • Information that includes the signals on top of each other.
  • a learning structure a suitably-conditioned characteristic map, a parameterizable mathematical function with suitably set parameters, a neural network and / or another suitable structure can be used.
  • the implementation of such a learning structure may be more complex than that of an analytical function, and the evaluation typically requires a higher one
  • a development of the invention provides that the learning structure is learned by considering a multiplicity of specific combinations of signal values, wherein each specific combination is assigned a value for the signal to be corrected.
  • each specific combination of erroneous signal values is assigned a true value for the signal to be corrected.
  • each specific combination of erroneous signal values is preferred for an individual combination of sensors and / or actuators with specific, individual, systematic deviations from ideal behavior, ie for an imaginary internal combustion engine with a specific combination of individual sensors and / or actuators.
  • the learning structure is determined for each such combination, what the true value of the signal to be corrected looks when the selected specific combination of erroneous signal values occurs.
  • the, in particular each, specific combination of signal values corresponds to a combination of sensor and / or actuator errors, which are in particular systematic sensor and / or actuator errors.
  • the specific combination represents a specific selection of sensors and / or actuators, and thus a specific internal combustion engine. Since the learning structure for a plurality of such specific combinations is learned, it follows that the determined with their help correction value causes an improvement in the behavior of the internal combustion engine in the field of application on a statistical average. In general, the true value of the signal to be corrected, which is then determined in the context of the method, will be closer to the actual true value than the previously determined, erroneous value of the
  • a development of the invention provides that the learning structure is taught on a test bench or by numerical simulation of the internal combustion engine. If the learning structure is taught in on a test stand, highly accurate sensors and / or actuators and therefore as error-free as possible signals are preferably used, in particular to acquire information about true values of a signal to be corrected.
  • Internal combustion engine is taught.
  • teaching the learning structure on the test bench can be carried out very precisely and in principle more realistically, learning the learning structure through numerical simulation is quicker and less complicated, and thus more cost effective overall.
  • the object is also achieved by providing a method for determining a learning structure for the operation of an internal combustion engine with the steps of claim 8.
  • the method is used in particular for determining a learning structure for use as
  • a multiplicity of specific combinations of faulty signal values of sensors and / or actuators of an internal combustion engine are selected. For each of these specific combinations, a true value of at least one signal to be corrected is determined and the true values are assigned to the associated specific combinations of erroneous signal values.
  • Learning structure progressively forms a connection, in particular a physical relationship, between the different signal values during learning, or creates an approximation to a true relationship between the signal values.
  • the learning structure learns on the basis of many different combinations of errors of individual sensors and / or actuators, which errors
  • the learning structure can then be used to observe and correct various signals on an internal combustion engine in real time.
  • the aforementioned method proposes a correction of one or more signals, that is to say measured variables or manipulated variables, based on the lem structure for a specific combination of sensor and / or actuator errors.
  • the learning structure used is preferably a neural network, a parameterizable mathematical function, a map or another suitable structure.
  • a neural network is taught by creating a variety of links, as is known per se.
  • parameter values are specified during teach-in.
  • the characteristic map is bedatet when teaching in a suitable manner.
  • the signals to be considered are preferably selected by means of a correlation matrix, which contains information about the correlation of the various measurement and control signals to the signal to be corrected. It is then determined a predetermined limit correlation, up to which signals still in the
  • the number of signals considered can be reduced, thus reducing the complexity of the process. This will also sink the Costs associated with the application of the method in the field of application.
  • the selection of signals is targeted and based on a meaningful criterion.
  • a development of the invention provides that a plurality of operating points of an internal combustion engine is selected, wherein a plurality of specific combinations of faulty signal values is selected for each operating point. This is significant because the relationship between the different signals from the operating point of the
  • a development of the invention provides that the learning structure is taught in on a test bench or by numerical simulation.
  • the method is thus preferably carried out on a test bench or by numerical simulation - virtually on a virtual test bench.
  • the learning structure is preferably trained as follows: The learning structure observes various sensor signals and actuator signals at a specific operating point of the internal combustion engine and an associated real value, ie measured or simulated virtually without errors under laboratory conditions signal to be corrected. It will be in the
  • the learning structure learns a dependency on the true value of it
  • Relationship between the observed signal values and the true signal value of the signal to be corrected describes with the support points that are available for learning, can not be ideally mapped. It is therefore possible that the learning structure instead of a For example, the exact solution minimizes a mean squared error between an estimate and the actual true function value.
  • the real functional value is known in particular either from bench tests or from numerical simulation.
  • a further development of the invention provides that statistical information about sensors and / or actuators is used to select the specific combinations. It does not take into account the statistical errors of the individual sensors, ie their measurement noise, but rather the systematic deviations of the sensors and / or actuators, namely deviations from sensor to sensor or from actuator to actuator. Such statistical
  • Information is ultimately a series spread of the installed sensors and / or actuators. They can be obtained from data sheets or also from measurements of a large number of sensors and / or actuators.
  • the invention also provides a method for operating a test bench, wherein a plurality of specific combinations of faulty signal values of sensors and / or actuators of an internal combustion engine are specified on the test bench, in particular by dimming signals, wherein a true value of at least one signal to be corrected for each of the specific combinations is determined.
  • a learning structure is learned by assigning the determined true values to the associated specific combinations of erroneous signal values.
  • an embodiment of the method for operating the internal combustion engine according to one of the embodiments described above is particularly preferred, which is characterized in that a learning structure which was determined as part of an embodiment of the method for determining a learning structure is used as a predetermined relationship.
  • the advantages described above are realized in a special way.
  • the correction of a measured combustion air ratio can serve.
  • the learning structure simultaneously observes flap positions of the internal combustion engine, a boost pressure, a
  • the true value of the signal to be corrected such as the true combustion air ratio
  • the learning structure is therefore used as follows: instead of the true value, it now outputs information about the learned function value for the true value, for example for the true combustion air ratio, when various error-related measuring and actuating signals are applied as an estimated value. This information then corrects the measured value of the signal to be corrected.
  • a development of the invention provides that the method for operating a
  • a development of the invention provides that internal combustion engines from the series, series or fleet are discarded if they do not meet predetermined, in particular legal requirements when using the method. For example, it is possible for each internal combustion engine after its production to undergo a test run in which it is checked whether it meets predetermined specifications. If the method proposed here is used in the operation of the internal combustion engine and shows that the internal combustion engine does not meet the specifications, it can be discarded.
  • control device for an internal combustion engine which is characterized in that it is adapted to carry out a
  • a development of the invention provides that the method is firmly implemented in an electronic structure, in particular in a hardware structure of the control unit.
  • a computer program product it is possible for a computer program product to be loaded into the control unit which has instructions on the basis of which the method is carried out when the computer program product is running on the control unit. It requires no further modification of a control unit, which for
  • Operation of the internal combustion engine is provided anyway, because this is anyway set up for detecting a plurality of signals and / or outputting a plurality of signals, wherein substantially the predetermined relationship, in particular the learning structure, in the control unit to implement, in order anyway to evaluate and correct the signals detected or output by the control unit in the appropriate manner.
  • the internal combustion engine is preferably designed as a reciprocating engine. At a
  • the internal combustion engine is used to drive in particular heavy land or water vehicles, such as mining vehicles, trains, the internal combustion engine is used in a locomotive or a railcar, or ships. It is also possible to use the internal combustion engine to drive a defense vehicle, for example a tank.
  • An embodiment of the internal combustion engine is preferably also stationary, for example, for stationary
  • the internal combustion engine in this case preferably drives a generator.
  • Internal combustion engine in the field of the extraction of fossil raw materials and in particular fuels, for example, oil and / or gas, possible. It is also possible to use the internal combustion engine in the industrial sector or in the field of construction, for example in a construction or construction machine, for example in a crane or an excavator. Finally, an application of the internal combustion engine in a passenger car or in a
  • the internal combustion engine is preferably designed as a diesel engine, as a gasoline engine, as a gas engine for operation with natural gas, biogas, special gas or another suitable gas. Especially if the
  • Internal combustion engine is designed as a gas engine, it is for use in one
  • Cogeneration plant suitable for stationary power generation.
  • Figure 1 is a schematic representation of a first embodiment of the method for
  • Figure 2 is a schematic representation of an embodiment of a method for
  • FIG. 3 shows a schematic illustration of a second embodiment of the method for operating an internal combustion engine using a learning structure determined according to the method according to FIG. 3
  • Figure 4 is a schematic representation of an embodiment of a
  • Fig. 1 shows a schematic representation of a first embodiment of the method for operating an internal combustion engine 1.
  • the internal combustion engine 1 is shown here symbolically, for better understanding of the method, a control unit 3 than from the
  • Internal combustion engine 1 is shown outsourced. However, the control unit 3 can also be part of the internal combustion engine 1.
  • a plurality of signals of the internal combustion engine 1 is detected, wherein these signals are generated by at least one sensor and / or sent to at least one actuator.
  • Actuator signals includes, which are also referred to as measurement signals 5 and 7 as control signals. These measurement and control signals 5, 7 are evaluated, and a specific combination of signal values is determined, wherein a predetermined relationship 9 for a plurality of internal combustion engines is predetermined in the form of an algorithm by which the detected signals are evaluated, the specific combination of the signal values and a correction value for at least one signal to be corrected is calculated.
  • a first correction value 11 for a measurement signal 5 and a second correction value 13 for an uncorrected adjustment signal 15 are determined here. With the correction values 11, 13 is then to be corrected
  • Measuring signal 5 and a correcting the control signal 15 corrected The corrected measurement signal 5 is input to a motor management 17, which generates altogether from the measurement signals 5 actuating signals, for example the uncorrected actuating signal 15, for the internal combustion engine 1.
  • the uncorrected actuating signal 15 is then corrected with the second correction value 13.
  • a corrected so far set of actuating signals 7 is then returned to the internal combustion engine 1.
  • the relationship 9 statistically takes into account the systematic deviations from the ideal behavior of the sensors and / or actuators installed on the internal combustion engine 1 and calculates the first and second correction values 11, 13 in such a way that an improvement of the operating behavior at least on average via a plurality of internal combustion engines 1 of a fleet and preferably a reduction in fuel consumption occurs.
  • an improvement in the operating behavior is not actually achieved for each specific internal combustion engine 1, but this applies only on a statistical average.
  • Adjusting signals 7 corrected, optionally by cascading the method In order to increase the accuracy of the method, a recursive application of the relationship 9 is also possible, in which the corrected signal values are supplied again to the relationship 9 and, in turn, new correction values are calculated therefrom.
  • the context 9 may be an analytical function.
  • a learning structure that is learned as part of a method for determining the learning structure is particularly preferred.
  • FIG. 2 shows a schematic representation of an embodiment of the method for determining such a learning structure 19. It is possible that the method is performed by numerical simulation, as it were, on a virtual test bench. Alternatively, it is possible that the method is performed on an actual test bench. The following is the
  • Combustion air ratio ⁇ are explained as a signal to be corrected.
  • the method is applicable to a variety of other to be corrected measuring or control signals 5, 7, so that the following explanations are merely illustrations of the
  • the internal combustion engine 1 is operated on a test bench under precisely controlled environmental conditions and with highly accurate measuring sensors as well as highly accurate actuators.
  • a multiplicity of true measured signal values 21 are determined with high accuracy.
  • true measured signal values here means that they are determined with a smaller, and in particular much less, error than is possible by means of the sensor technology of the internal combustion engine 1 used in the field of application true value, as measured values of a sensor used in the normal field of application.
  • the highly accurate measured value of a combustion air ratio is coupled out of the multiplicity of true measured signal values 21, which is also referred to as ⁇ value or, in the following, briefly as true value 23. This will be the
  • the engine management 17 calculates on the basis of the trimmed, thus erroneous measurement signals 5 the Control signals 15, which are faulty insofar as they do not correspond to the functional positions that are actually set by the faulty actuator of the internal combustion engine actually. This is simulated on the test bench by being targeted here
  • Actuator deviations 27 are added to the control signals 15. These are so intimated
  • Control signals 29 are preferably supplied to a high-precision actuator system of the internal combustion engine 1 on the test bench, which is then actuated quasi systematically error-prone with the trimmed control signals 29. In this way, systematic actuator errors of real actuators actually installed in the fleet of internal combustion engines are simulated.
  • the learning structure 19 are in turn supplied to the control signals 15, which are faulty insofar as they do not correspond to the actual An Kunststoffang the internal combustion engine 1, namely on the test bench due to the trim, but in the real field of application due to the systematic deviation of the concrete installed actuators.
  • a variety of specific combinations of erroneous signal values is through
  • the learning structure now learns via this multitude of combinations the relationship between the measurement signals 5, the control signals 15, and the true values 23, which are established in the corresponding faulty drive range of the internal combustion engine 1.
  • This learning can be done, for example, by parameterizing a parameterizable mathematical function, by linking a neural network or by using a corresponding characteristic map.
  • the learning structure 19 learns the relationship between the control behavior of the engine management 17 and the resulting true values 23 of the internal combustion engine 1, in this case specifically the true value of the combustion air ratio.
  • Flap positions in particular a charge air flap and / or an exhaust gas recirculation flap, a Boost pressure, a charge air temperature, the measured combustion air ratio itself, and the nitrogen oxide emissions of the internal combustion engine 1.
  • the flap positions are control signals
  • the boost pressure, the charge air temperature, the combustion ratio itself and the nitrogen oxide emissions are measurement signals.
  • the method is preferably carried out for a plurality of operating points of the internal combustion engine, wherein the learning structure 19 learns an operating-point-dependent relationship between the signals 5, 15 and the true values 23. For each operating point, a multiplicity of deviations 25, 27 is specified for which the learning structure 19 learns the relationship to the then present true values 23.
  • FIG. Fig. 3 shows a further concrete embodiment or a second
  • Embodiment of the method for operating an internal combustion engine in which case the learning structure 19 is used as context 9. It is symbolically indicated here that the actual measured signal values 21 are not falsified by specific deviations 25 in the application field of the internal combustion engine 1, but by systematic errors 31 of the sensors, so that the engine management 17 is faulty
  • Measuring signals 5 are supplied.
  • Combustion air ratio is coupled as a signal to be corrected 33.
  • the learning structure 19 the error-prone measuring signals 5, as well as those of the
  • Motor management 17 output control signals 15 supplied.
  • the learning structure 19 calculates therefrom a correction value 11 for the combustion air ratio, this is corrected by means of the correction value, and the motor management 17 is thus supplied with a corrected value 35.
  • the engine management 17 thus calculates the actuating signals 15 on the basis of uncorrected subcombination 5 'and the corrected value 35 for the
  • Air-fuel ratio As already indicated, it is of course possible for a plurality of measuring and / or actuating signals to be corrected in the manner described here by way of example. In particular, it is also possible that all measurement signals are corrected in this way.
  • FIG. 4 shows still a schematic representation of an embodiment of a
  • control unit 3 is operatively connected to the sensor 39 and the actuator 41, wherein it controls the internal combustion engine 1 on the basis of the signals detected by the sensors 39 and by means of the signals output to the actuators 41.
  • control unit 3 is a context 9 - as explained for Figure 1 - particularly preferably a learning structure 19 - as explained for Figure 3 - implemented.

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Combined Controls Of Internal Combustion Engines (AREA)

Abstract

L'invention concerne un procédé permettant de faire fonctionner un moteur à combustion interne (1), et comprenant les étapes suivantes : l'acquisition d'une pluralité de signaux (5, 7) qui sont produits par au moins un capteur (39) et/ou envoyés à au moins un actionneur (41) ; l'évaluation des signaux acquis (5, 7) et la détermination d'une combinaison spécifique des valeurs des signaux (5, 7) ; la détermination d'une valeur de correction (11, 13) pour au moins un signal à corriger (15, 33) sur la base d'une relation (9) déterminée préalablement pour une pluralité de moteurs à combustion interne entre la combinaison spécifique des valeurs des signaux acquises et le signal à corriger (15, 33) ; et la correction de la valeur du signal à corriger (15, 33) au moyen de la valeur de correction (11).
PCT/EP2015/000508 2014-03-26 2015-03-06 Procédé permettant de faire fonctionner un moteur à combustion interne, procédé permettant de déterminer une structure d'apprentissage pour le fonctionnement d'un moteur à combustion interne, appareil de commande d'un moteur à combustion interne, et moteur à combustion interne WO2015144287A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102014205686.1 2014-03-26
DE102014205686.1A DE102014205686A1 (de) 2014-03-26 2014-03-26 Verfahren zum Betreiben einer Brennkraftmaschine, Verfahren zum Ermitteln einer Lernstruktur für den Betrieb einer Brennkraftmaschine, Steuergerät für eine Brennkraftmaschine und Brennkraftmaschine

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WO2015144287A1 true WO2015144287A1 (fr) 2015-10-01

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Cited By (1)

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CN111133184A (zh) * 2017-09-08 2020-05-08 维特思科科技有限责任公司 用于检查在运行时在内燃机的空气进气管段或者废气排放管段中的压力传感器的功能的方法以及发动机控制单元

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