DE10203920A1 - Method for determining the exhaust gas temperature in the exhaust system of a combustion engine employs a model based on part- models with at least the engine represented as a neuronal network - Google Patents

Method for determining the exhaust gas temperature in the exhaust system of a combustion engine employs a model based on part- models with at least the engine represented as a neuronal network

Info

Publication number
DE10203920A1
DE10203920A1 DE10203920A DE10203920A DE10203920A1 DE 10203920 A1 DE10203920 A1 DE 10203920A1 DE 10203920 A DE10203920 A DE 10203920A DE 10203920 A DE10203920 A DE 10203920A DE 10203920 A1 DE10203920 A1 DE 10203920A1
Authority
DE
Germany
Prior art keywords
combustion engine
exhaust system
models
internal combustion
model
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
Application number
DE10203920A
Other languages
German (de)
Inventor
Heiko Konrad
Christoph Luttermann
Franz Perschl
Alexander Mitterer
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Bayerische Motoren Werke AG
Original Assignee
Bayerische Motoren Werke AG
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 Bayerische Motoren Werke AG filed Critical Bayerische Motoren Werke AG
Priority to DE10203920A priority Critical patent/DE10203920A1/en
Publication of DE10203920A1 publication Critical patent/DE10203920A1/en
Ceased legal-status Critical Current

Links

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
    • F02D41/02—Circuit arrangements for generating control signals
    • F02D41/021—Introducing corrections for particular conditions exterior to the engine
    • F02D41/0235—Introducing corrections for particular conditions exterior to the engine in relation with the state of the exhaust gas treating apparatus
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N9/00—Electrical control of exhaust gas treating apparatus
    • F01N9/005—Electrical control of exhaust gas treating apparatus using models instead of sensors to determine operating characteristics of exhaust systems, e.g. calculating catalyst temperature instead of measuring it directly
    • 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
    • F02D41/02—Circuit arrangements for generating control signals
    • F02D41/14—Introducing closed-loop corrections
    • F02D41/1438—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor
    • F02D41/1444—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor characterised by the characteristics of the combustion gases
    • F02D41/1446—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor characterised by the characteristics of the combustion gases the characteristics being exhaust temperatures
    • F02D41/1447—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor characterised by the characteristics of the combustion gases the characteristics being exhaust temperatures with determination means using an estimation
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • 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
    • F02D41/02—Circuit arrangements for generating control signals
    • F02D41/14—Introducing closed-loop corrections
    • F02D41/1401—Introducing closed-loop corrections characterised by the control or regulation method
    • F02D41/1405—Neural network control
    • 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

Abstract

Method for determining the exhaust gas temperature in the exhaust system of a combustion engine using a neuronal network. Accordingly the whole system of combustion engine and exhaust gas system is subdivided into part models, from which at least the combustion engine is represented as a neuronal network, while other part-models of the exhaust gas system can be represented as a physical model or a static or dynamic neuronal network.

Description

Die Erfindung betrifft ein Verfahren zur Bestimmung der Abgastemperatur in der Abgasanlage einer Brennkraftmaschine mit neuronalen Netzen. Zum technischen Umfeld wird auf die EP 0 877 309 B1 verwiesen. The invention relates to a method for determining the exhaust gas temperature in the exhaust system of an internal combustion engine with neural networks. To the technical environment, reference is made to EP 0 877 309 B1.

Bekanntermaßen benötigen Steuerfunktionen für moderne Brennkraftmaschinen bzw. Verbrennungsmotoren auch Informationen über die Abgastemperatur, bspw. für den Bauteilschutz von Katalysatoren. Im unteren Temperaturbereich etwa ist der Einschalt- und Diagnosezeitpunkt der Lambdaregelung abhängig von der Abgastemperatur. Zum Schutz temperaturempfindlicher Bauteile in der Abgasanlage erhöhen Ottomotoren zum Teil die Kraftstoff-Einspitzmenge, wenn eine Bauteilschädigung droht. Das genaue Einstellen dieser Anfettungsmenge ist sowohl aus Umwelt- als auch aus Verbrauchsgründen ein wesentliches Ziel. Daher ist die möglichst genaue Kenntnis der Abgastemperaturen im oberen Temperaturbereich von besonderer Bedeutung. As is known, control functions are required for modern ones Internal combustion engines or internal combustion engines also provide information about the Exhaust gas temperature, for example for component protection of catalysts. At the bottom The temperature range, for example, is the start and diagnosis time of Lambda control depending on the exhaust gas temperature. For protection Temperature-sensitive components in the exhaust system partially increase gasoline engines Fuel injection quantity when there is a risk of component damage. The exact Setting this enrichment amount is both from environmental as well An essential goal for reasons of consumption. Therefore, it is the most accurate Knowledge of the exhaust gas temperatures in the upper temperature range of special meaning.

Da die Abgastemperaturen im Fahrzeug nur sehr aufwändig messbar sind, werden üblicherweise Modellgrößen verwendet. Derzeitige Steuerungssysteme für Verbrennungsmotoren umfassen zum Teil physikalisch basierte Modelle, um die Temperatur zu berechnen. Ein großer Teil der Modelle besteht allerdings aus vorsteuernden Kennfeldern, d. h. in Abhängigkeit vom Betriebspunkt der Brennkraftmaschine sowie weiterer Eingangsgrößen sind quasi Sollwerte für bestimmte Größen abgelegt. Since the exhaust gas temperatures in the vehicle can only be measured with great effort, model sizes are usually used. current Control systems for internal combustion engines partly include physically based ones Models to calculate the temperature. Much of the models However, it consists of pilot control maps, d. H. depending on Operating point of the internal combustion engine and other input variables are quasi setpoints for certain sizes.

Derartige physikalische und kennfeldbasierte Modelle können praktisch nicht alle wesentlichen Einflussgrößen berücksichtigen, was zu einer unzureichenden Genauigkeit der Modelltemperaturen führt. Ferner wird das dynamische Temperaturverhalten zu ungenau nachgebildet, und zwar insbesondere bei sprunghaften Änderungen der Betriebsparameter. Ein weiterer Nachteil besteht darin, dass bei einer Änderung der Grundapplikation insbesondere des Steuerungssystems der Brennkraftmaschine oder von temperaturrelevanten Bauteilen eine weitgehende Neuapplikation der Modelle durchgeführt werden muss, was mit hohem Aufwand verbunden ist. Such physical and map-based models can practically not take into account all significant influencing factors, which leads to insufficient accuracy of the model temperatures. Furthermore, that dynamic temperature behavior too imprecise, namely especially in the event of sudden changes in operating parameters. On Another disadvantage is that when the Basic application in particular of the control system of the internal combustion engine or of temperature-relevant components a largely new application of Models must be carried out, which is associated with great effort.

Ein anderer, in der o. g. EP 0 877 309 B1 beschriebener Ansatz verwendet ein neuronales Netz als virtuellen Sensor. Das neuronale Netz bestimmt hierbei Polynomkoeffizienten als Funktion von gemessenen physikalischen Größen, die am Verbrennungsmotor bereits vorhanden sind. Das sog. Netz- Training erfolgt aus Daten, die aus einem Simulationsmodell erzeugt wurden. Das Simulationsmodell wiederum wird aus Komponentenmessungen bestimmt. Als mögliche Applikation ist die Bestimmung der Abgastemperatur angegeben. Another, in the above Approach described in EP 0 877 309 B1 a neural network as a virtual sensor. The neural network determines here polynomial coefficients as a function of measured physical Sizes that are already available on the internal combustion engine. The so-called network Training takes place from data generated from a simulation model. The simulation model in turn is made up of component measurements certainly. The determination of the exhaust gas temperature is a possible application specified.

Die in der EP 0 877 309 B1 vorgeschlagene Vorgehensweise beim Netz- Training via Simulationsmodell ist sehr aufwändig. Durch den Polynomansatz ist die Genauigkeit der Modellabbildung gegenüber einer direkten Modell-Beschreibung durch ein neuronales Netz niedriger. Ferner liegt keine Aussage bzgl. der Berücksichtigung der Prozessdynamik vor. The procedure proposed in EP 0 877 309 B1 for network Training via the simulation model is very complex. By the Polynomial approach is the accuracy of the model mapping compared to a direct one Model description by a neural network lower. Furthermore, there is none Statement regarding the consideration of the process dynamics.

Ein demgegenüber verbessertes Verfahren nach dem Oberbegriff des Anspruchs 1 aufzuzeigen ist daher Aufgabe der vorliegenden Erfindung. In contrast, an improved method according to the preamble of To point claim 1 is therefore an object of the present invention.

Die Lösung dieser Aufgabe ist dadurch gekennzeichnet, dass das aus der Brennkraftmaschine und der Abgasanlage bestehende Gesamtsystem in einzelne Teil-Modelle zerlegt ist, von denen zumindest die Brennkraftmaschine in Form eines neuronalen Netzes abgebildet ist, während weitere Teil-Modelle der Abgasanlage in Form eines physikalischen Modells oder in Form eines statischen oder dynamischen neuronalen Netzes abgebildet sind. Vorteilhafte Weiterbildungen sind Inhalt der Unteransprüche. The solution to this problem is characterized in that from the Internal combustion engine and the exhaust system existing overall system in individual partial models is disassembled, at least of which Internal combustion engine is mapped in the form of a neural network while others Part models of the exhaust system in the form of a physical model or in Mapped form of a static or dynamic neural network are. Advantageous further developments are the content of the subclaims.

Der neue hiermit vorgeschlagene Ansatz sieht vor, die Abhängigkeit der Abgastemperaturen von verschiedenen Betriebsbedingungen der Brennkraftmaschine durch Teil-Modelle abzubilden. Diese Teil-Modelle umfassen ein oder mehrere künstliche neuronale Netze, ggf. können zusätzlich Teile des Modells physikalisch basiert sein. Die neuronalen Teilmodelle können statisch oder dynamisch sein. Bezogen auf die verschiedenen Modelltemperaturen in der Abgasanlage sind unterschiedliche Kombinationsmöglichkeiten der Teilmodelle denkbar, vgl. hierzu die beigefügte Figurendarstellung. Dabei können statische und dynamische Neuronale Netze sowie physikalische Modelle wahlweise parallel oder seriell angeordnet sein. Bevorzugt wird dabei die Temperatur (TVkat) an einem Vor-Katalysator (Vorkat) in der Abgasanlage aus einem neben dem Brennkraftmaschinen-Teilmodell (BKM) ersten Teilmodell der Abgasanlage und die Temperatur (TKat) an einem dem Vorkatalysator nachgeschalteten Haupt-Katalysator (Kat) aus einem zweiten Teilmodell der Abgasanlage bestimmt. The new approach proposed hereby provides for the dependency of the exhaust gas temperatures on different operating conditions of the internal combustion engine to be represented by partial models. These sub-models comprise one or more artificial neural networks; if necessary, parts of the model can also be physically based. The neural sub-models can be static or dynamic. Based on the different model temperatures in the exhaust system, different possible combinations of the sub-models are conceivable, cf. the attached figure representation. Static and dynamic neural networks as well as physical models can be arranged either in parallel or in series. The temperature (T Vkat ) on a pre-catalytic converter (pre-cat) in the exhaust system from a first partial model of the exhaust gas system in addition to the internal combustion engine sub-model (BKM) and the temperature (T Kat ) on a main catalytic converter connected downstream of the pre-catalyst is preferred ( Kat) determined from a second partial model of the exhaust system.

Modell-Eingangsgrößen der neuronalen oder physikalischen Teilmodelle sind u. a. die mit den vorhandenen physikalischen Sensoren gemessenen Größen bzw. die Ausgangsgrößen der vorgeschalteten Teilmodelle. Insbesondere die relevanten Eingangsgrößen für das Brennkraftmaschinen- Teilmodell werden physikalisch gemessen, wie bspw. die angesaugte Luftmasse, die Ansauglufttemperatur, der Lambdawert usw., während die Eingangsgrößen der nachgeschalteten Teilmodelle die Ausgangsgrößen des jeweils vorgeschalteten Teilmodells sind. Model input variables of the neural or physical sub-models are u. a. those measured with the existing physical sensors Sizes or the output sizes of the upstream sub-models. In particular, the relevant input variables for the internal combustion engine Partial models are measured physically, such as the suctioned one Air mass, intake air temperature, lambda value, etc., while the Input variables of the downstream sub-models the output variables of the are upstream partial models.

Bevorzugt findet der Abgleich der neuronalen Modelle in einer Trainingsphase auf Basis von Referenz-Temperaturmessungen an der Abgasanlage statt. Die Temperaturmodelle sind in erster Linie für den online-Einsatz in der Steuerungselektronik für die Brennkraftmaschine vorgesehen. Eine weitere Verwendung ist darüber hinaus in Simulationswerkzeugen möglich. The neural models are preferably compared in one Training phase based on reference temperature measurements on the exhaust system. The temperature models are primarily for online use in the Control electronics provided for the internal combustion engine. Another It can also be used in simulation tools.

Claims (4)

1. Verfahren zur Bestimmung der Abgastemperatur in der Abgasanlage einer Brennkraftmaschine mit neuronalen Netzen, dadurch gekennzeichnet, dass das aus der Brennkraftmaschine und der Abgasanlage bestehende Gesamtsystem in einzelne Teil-Modelle zerlegt ist, von denen zumindest die Brennkraftmaschine in Form eines neuronalen Netzes abgebildet ist, während weitere Teil-Modelle der Abgasanlage in Form eines physikalischen Modells oder in Form eines statischen oder dynamischen neuronalen Netzes abgebildet sind. 1. A method for determining the exhaust gas temperature in the exhaust system of an internal combustion engine with neural networks, characterized in that the overall system consisting of the internal combustion engine and the exhaust system is broken down into individual partial models, of which at least the internal combustion engine is represented in the form of a neural network, while other partial models of the exhaust system are shown in the form of a physical model or in the form of a static or dynamic neural network. 2. Verfahren nach Anspruch 1, dadurch gekennzeichnet, dass die Temperatur an einem Vor-Katalysator aus einem neben dem Brennkraftmaschinen-Teilmodell ersten Teilmodell der Abgasanlage und die Temperatur an einem dem Vorkatalysator nachgeschalteten Haupt-Katalysator aus einem zweiten Teilmodell der Abgasanlage bestimmt wird. 2. The method according to claim 1, characterized in that the temperature at a pre-catalyst from a first sub-model in addition to the internal combustion engine sub-model the exhaust system and the temperature at the pre-catalyst downstream main catalyst from a second sub-model of the Exhaust system is determined. 3. Verfähren nach Anspruch 1 oder 2, dadurch gekennzeichnet, dass die Eingangsgrößen für das Brennkraftmaschinen-Teilmodell die mit vorhandenen physikalischen Sensoren gemessenen relevanten Größen (wie angesaugte Luftmasse, Ansauglufttemperatur, Lambdawert usw.) sind, und dass die Eingangsgrößen der nachgeschalteten Teilmodelle die Ausgangsgrößen des jeweils vorgeschalteten Teilmodells sind. 3. Procedure according to claim 1 or 2, characterized in that the input variables for the Internal combustion engine sub-model with existing physical sensors relevant variables measured (such as intake air mass, intake air temperature, Lambda value, etc.) and that the input variables of the downstream Submodels the output variables of the upstream submodel are. 4. Verfahren nach einem der vorangegangenen Ansprüche, dadurch gekennzeichnet, dass der Abgleich der neuronalen Modelle in einer Trainingsphase auf Basis von Referenz-Temperaturmessungen an der Abgasanlage erfolgt. 4. The method according to any one of the preceding claims, characterized in that the comparison of the neural models in one Training phase based on reference temperature measurements at the Exhaust system takes place.
DE10203920A 2002-01-31 2002-01-31 Method for determining the exhaust gas temperature in the exhaust system of a combustion engine employs a model based on part- models with at least the engine represented as a neuronal network Ceased DE10203920A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
DE10203920A DE10203920A1 (en) 2002-01-31 2002-01-31 Method for determining the exhaust gas temperature in the exhaust system of a combustion engine employs a model based on part- models with at least the engine represented as a neuronal network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
DE10203920A DE10203920A1 (en) 2002-01-31 2002-01-31 Method for determining the exhaust gas temperature in the exhaust system of a combustion engine employs a model based on part- models with at least the engine represented as a neuronal network

Publications (1)

Publication Number Publication Date
DE10203920A1 true DE10203920A1 (en) 2003-09-04

Family

ID=27674545

Family Applications (1)

Application Number Title Priority Date Filing Date
DE10203920A Ceased DE10203920A1 (en) 2002-01-31 2002-01-31 Method for determining the exhaust gas temperature in the exhaust system of a combustion engine employs a model based on part- models with at least the engine represented as a neuronal network

Country Status (1)

Country Link
DE (1) DE10203920A1 (en)

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2867519A1 (en) * 2003-12-10 2005-09-16 Renault Sas Internal combustion engine controlling device, has delay units disposed in upstream of neural network for taking into account history of operation of internal combustion engine, and delay selection unit disposed in upstream of network
FR2879243A1 (en) * 2004-12-10 2006-06-16 Renault Sas Exhaust gas temperature estimation system for internal combustion engine, has control unit with neural networks receiving measured data and delivering data with treated exhaust gas temperature, and module to reroute latter data
EP1698776A1 (en) * 2005-03-01 2006-09-06 Delphi Technologies, Inc. Internal combustion engine control system
DE102006007417A1 (en) * 2006-02-17 2007-08-30 Siemens Ag Method and device for operating an internal combustion engine
DE102007008514A1 (en) 2007-02-21 2008-09-04 Siemens Ag Method and device for neuronal control and / or regulation
DE102007012820A1 (en) * 2007-03-17 2008-09-18 Ford Global Technologies, LLC, Dearborn Exhaust gas after-treatment device temperature controlling method for diesel engine of motor vehicle, involves measuring exhaust gas temperature supplied as input signal of model of exhaust gas after-treatment device over sensor model
WO2009112056A1 (en) * 2008-03-14 2009-09-17 Fev Motorentechnik Gmbh Cylinder pressure guided regeneration operation and operation type change
US7664593B2 (en) * 2004-10-06 2010-02-16 Renault S.A.S. Method and system for estimating exhaust gas temperature and internal combustion engine equipped with such a system
DE102014000395A1 (en) 2014-01-17 2015-07-23 Fev Gmbh Method for controlling an internal combustion engine
DE102022202013A1 (en) 2022-02-28 2023-08-31 Psa Automobiles Sa Method for controlling the exhaust gas temperature for a motor vehicle powered by an internal combustion engine and engine control of a motor vehicle
WO2023242137A1 (en) * 2022-06-13 2023-12-21 Siemens Aktiengesellschaft Controlling a starting and/or shutdown process and/or load change of a technical installation

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2867519A1 (en) * 2003-12-10 2005-09-16 Renault Sas Internal combustion engine controlling device, has delay units disposed in upstream of neural network for taking into account history of operation of internal combustion engine, and delay selection unit disposed in upstream of network
US7664593B2 (en) * 2004-10-06 2010-02-16 Renault S.A.S. Method and system for estimating exhaust gas temperature and internal combustion engine equipped with such a system
FR2879243A1 (en) * 2004-12-10 2006-06-16 Renault Sas Exhaust gas temperature estimation system for internal combustion engine, has control unit with neural networks receiving measured data and delivering data with treated exhaust gas temperature, and module to reroute latter data
EP1698776A1 (en) * 2005-03-01 2006-09-06 Delphi Technologies, Inc. Internal combustion engine control system
DE102006007417B4 (en) * 2006-02-17 2012-08-09 Continental Automotive Gmbh Method and device for operating an internal combustion engine
US8224553B2 (en) 2006-02-17 2012-07-17 Continental Automotive Gmbh Method and device for operating an internal combustion engine
DE102006007417A1 (en) * 2006-02-17 2007-08-30 Siemens Ag Method and device for operating an internal combustion engine
DE102007008514A1 (en) 2007-02-21 2008-09-04 Siemens Ag Method and device for neuronal control and / or regulation
DE102007012820A1 (en) * 2007-03-17 2008-09-18 Ford Global Technologies, LLC, Dearborn Exhaust gas after-treatment device temperature controlling method for diesel engine of motor vehicle, involves measuring exhaust gas temperature supplied as input signal of model of exhaust gas after-treatment device over sensor model
WO2009112056A1 (en) * 2008-03-14 2009-09-17 Fev Motorentechnik Gmbh Cylinder pressure guided regeneration operation and operation type change
DE102014000395A1 (en) 2014-01-17 2015-07-23 Fev Gmbh Method for controlling an internal combustion engine
DE102022202013A1 (en) 2022-02-28 2023-08-31 Psa Automobiles Sa Method for controlling the exhaust gas temperature for a motor vehicle powered by an internal combustion engine and engine control of a motor vehicle
WO2023242137A1 (en) * 2022-06-13 2023-12-21 Siemens Aktiengesellschaft Controlling a starting and/or shutdown process and/or load change of a technical installation

Similar Documents

Publication Publication Date Title
DE19915047C2 (en) Method and device for regulating the fuel supply to an engine
DE102011085115B4 (en) Method and device for adapting a lambda control
DE4341132C2 (en) Air / fuel ratio calculator for multi-cylinder internal combustion engines
WO2018091252A1 (en) Method for controlling an exhaust gas component filling level in an accumulator of a catalytic converter
DE3533287C2 (en)
DE10203920A1 (en) Method for determining the exhaust gas temperature in the exhaust system of a combustion engine employs a model based on part- models with at least the engine represented as a neuronal network
DE4039876A1 (en) DEVICE FOR REGULATING THE AIR FUEL RATIO FOR AN ENGINE
EP3194750A1 (en) Method for detecting defective injection nozzles of an internal combustion engine
DE4140527A1 (en) CONTROL DEVICE FOR THE AIR / FUEL RATIO FOR USE IN AN INTERNAL COMBUSTION ENGINE
DE102018251720A1 (en) Method for determining a maximum storage capacity of an exhaust gas component storage device of a catalytic converter
DE102018216980A1 (en) Method for regulating a filling of a storage device of a catalytic converter for an exhaust gas component as a function of aging of the catalytic converter
EP1402240A1 (en) Method and device for measuring a temperature variable in a mass flow pipe
EP0805914B1 (en) Process for regulating the fuel-oxygen ratio of exhaust gas upstream of a catalytic converter
DE3835766A1 (en) ELECTRONIC, ADAPTABLE CONTROL UNIT FOR A COMBUSTION ENGINE
DE102012200032B4 (en) Method and device for dynamic diagnosis of sensors
DE102008004218B4 (en) Procedure for determining the dynamic soot emission
DE102004040273B4 (en) Cylinder air mass flow prediction model
EP2786003B1 (en) Method and apparatus for controlling an air fuel ratio of an internal combustion engine
DE602004007680T2 (en) Control system and method for an internal combustion engine with a three-way catalyst
WO2009040293A1 (en) Method and device for determining a dynamic property of an exhaust gas sensor
DE102006059673A1 (en) System for determining operational value in exhaust gas system of internal combustion engine of vehicle, has model simulation of exhaust gas plant components of exhaust gas plant, where model has inlets and outlet
DE4033026C2 (en) Method for avoiding thermally critical states of an exhaust gas aftertreatment device for an internal combustion engine
DE19725567A1 (en) Fault diagnosis system for air/fuel ratio control system of IC engine
DE102022108459B3 (en) Computer-implemented method for creating a mathematical formula for calculating emissions measurements from an internal combustion engine
DE602004011732T2 (en) Control method for achieving a linear shear response

Legal Events

Date Code Title Description
OP8 Request for examination as to paragraph 44 patent law
OR8 Request for search as to paragraph 43 lit. 1 sentence 1 patent law
8105 Search report available
8131 Rejection