WO2020176914A1 - Procédé et système de commande et/ou de régulation d'au moins un composant de post-traitement de gaz d'echappement - Google Patents

Procédé et système de commande et/ou de régulation d'au moins un composant de post-traitement de gaz d'echappement Download PDF

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Publication number
WO2020176914A1
WO2020176914A1 PCT/AT2020/060055 AT2020060055W WO2020176914A1 WO 2020176914 A1 WO2020176914 A1 WO 2020176914A1 AT 2020060055 W AT2020060055 W AT 2020060055W WO 2020176914 A1 WO2020176914 A1 WO 2020176914A1
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WO
WIPO (PCT)
Prior art keywords
exhaust gas
aftertreatment component
exhaust
model
gas aftertreatment
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
PCT/AT2020/060055
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German (de)
English (en)
Inventor
Uwe Martin
Boris DI BULATOVIC
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.)
AVL List GmbH
Original Assignee
AVL List 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 AVL List GmbH filed Critical AVL List GmbH
Priority to DE112020001020.1T priority Critical patent/DE112020001020A5/de
Publication of WO2020176914A1 publication Critical patent/WO2020176914A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • 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
    • 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
    • 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
    • F01N3/00—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust
    • F01N3/08—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous
    • F01N3/10—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous by thermal or catalytic conversion of noxious components of exhaust
    • F01N3/18—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous by thermal or catalytic conversion of noxious components of exhaust characterised by methods of operation; Control
    • 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
    • F01N3/00—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust
    • F01N3/08—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous
    • F01N3/10—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous by thermal or catalytic conversion of noxious components of exhaust
    • F01N3/18—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous by thermal or catalytic conversion of noxious components of exhaust characterised by methods of operation; Control
    • F01N3/20—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous by thermal or catalytic conversion of noxious components of exhaust characterised by methods of operation; Control specially adapted for catalytic conversion
    • 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
    • F01N3/00—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust
    • F01N3/08—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous
    • F01N3/10—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous by thermal or catalytic conversion of noxious components of exhaust
    • F01N3/18—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous by thermal or catalytic conversion of noxious components of exhaust characterised by methods of operation; Control
    • F01N3/20—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for rendering innocuous by thermal or catalytic conversion of noxious components of exhaust characterised by methods of operation; Control specially adapted for catalytic conversion
    • F01N3/206—Adding periodically or continuously substances to exhaust gases for promoting purification, e.g. catalytic material in liquid form, NOx reducing agents
    • F01N3/2066—Selective catalytic reduction [SCR]
    • 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
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
    • Y02A50/20—Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters
    • 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
    • Y02T10/12—Improving ICE efficiencies
    • 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 and a control device according to the features of the preambles of the independent claims.
  • the invention relates to a possibility for gas concentration calculation with the aid of a neural network.
  • a control device is usually provided, which is usually part of the engine control device or in particular is operated at least by the same processing unit.
  • a control device is usually provided, which is usually part of the engine control device or in particular is operated at least by the same processing unit.
  • models for individual or several exhaust gas aftertreatment components of the exhaust gas aftertreatment system are stored in the control device.
  • Such components can be used, for example, in a diesel engine
  • Diesel oxidation catalytic converter DOC
  • DPF diesel particulate filter
  • SCR system i.e. a system for selective catalytic reduction comprising a metering unit for an operating medium and an SCR catalytic converter.
  • control units and models for calculating the processes for Otto engines There are also control units and models for calculating the processes for Otto engines
  • the models used are often referred to as physical or kinetic models and correspond to mathematical representations of the chemical and physical reactions within the exhaust aftertreatment component. For example, in the case of a particle filter, the model can calculate the current particle load.
  • the components and catalysts are mapped along a flow direction of the exhaust gas by one-dimensional models, each in a fixed number of
  • the kinetic models must be adapted to actually occurring parameters, which can further increase the running time of the calculation.
  • the kinetic models must be adapted to actually occurring parameters, which can further increase the running time of the calculation.
  • more complex exhaust aftertreatment systems such as those used for
  • the object of the invention is now to provide a system for controlling and / or regulating at least one exhaust gas aftertreatment component of a
  • the simplified mathematical model is more efficient than the kinetic model.
  • the mathematical model created according to the invention can also be referred to as being simplified in comparison to the kinetic model.
  • the terms simplified mathematical model and more efficient mathematical model are used equivalently.
  • an artificial neural network is used and trained in such a way that it delivers essentially the same results as the kinetic model in question. So the two models do not have the same properties, but produce equivalent results.
  • the artificial neural network is preferably not trained with sensor data from a real exhaust gas aftertreatment system, but with data from the kinetic model to be replaced.
  • the training of the model is basically independent of a data source (measured or created by the kinetic model). This means that there is essentially no need to measure a real component. Provision can be made for data or parameters for the neural network to be generated over a wide range by varying a state of the kinetic model. For example, a temperature, a mass flow or a chemical composition of the exhaust gas is used as
  • State variable defined and / or used. It can also be advantageous when training with real sensor data from the parameters that actually occur is dependent. With learning data from a kinetic model, a free choice of parameters and states of the model can take place, whereby the training can be carried out over a wide range of parameters with relatively little effort.
  • the neural network is particularly preferably not only trained but also adapted, this preferably taking place simultaneously or iteratively.
  • Input values and output values of a kinetic model are preferably used as training data.
  • the neural network is trained and / or adapted until those output values are calculated for certain input values which correspond with sufficient accuracy to the corresponding output values of the kinetic model.
  • the trained neural network can itself on a system for control and / or regulation at least one
  • Exhaust aftertreatment component of an exhaust aftertreatment system of an internal combustion engine in particular on the engine control device or the control device of the exhaust aftertreatment system, are stored.
  • This simplified mathematical model can replace one or more kinetic models of the control and / or regulation of the exhaust gas aftertreatment system.
  • the computationally intensive and time-consuming kinetic model no longer needs to be used.
  • the chemical composition of the gas in the exhaust system can thus be modeled via the neural network.
  • the injection into the SCR is regulated.
  • others can also be used.
  • Exhaust aftertreatment components are simulated and controlled.
  • the input variables into the SCR are preferably a function of the output variables from the DOC.
  • the invention preferably relates to a method for creating a system for
  • Control and / or regulation of at least one exhaust gas aftertreatment component of an exhaust gas aftertreatment system of an internal combustion engine the system being a control device arranged in a vehicle for the
  • Exhaust gas aftertreatment system such as the engine control unit in particular, wherein the control device for controlling and / or regulating the
  • Exhaust aftertreatment component comprises a simplified mathematical model, wherein the simplified mathematical model performs a gas concentration calculation downstream of the exhaust aftertreatment component and wherein the simplified mathematical model is created using an artificial neural network.
  • the data in particular the input values and the output values of the kinetic model, are used to train the artificial neural network.
  • an adaptation of the parameters for the neural network is carried out and / or is necessary.
  • a number of layers or a number of neurons can be adapted.
  • the simplified mathematical model is the kinetic model of the system and in particular the kinetic model of the
  • the input values are one or more of the following exhaust gas aftertreatment parameters:
  • the neural network can in principle also be viewed as a regression formula, with an estimation being carried out. In principle, no additional regression formula is generated.
  • the exhaust gas aftertreatment component is a diesel oxidation catalytic converter (DOC) and that the simplified mathematical model calculates the gas concentration in and / or after a diesel oxidation catalytic converter (DOC).
  • DOC diesel oxidation catalytic converter
  • the exhaust gas aftertreatment component is an SCR system
  • the simplified mathematical model is the gas concentration of the NOx components of the exhaust gas before the SCR system, in particular the NOx concentration after the diesel oxidation catalytic converter and / or after the
  • Diesel particulate filter is calculated, and that the simplified mathematical model calculates the required fuel injection quantity. However, it can also calculate the required fuel injection quantity.
  • the exhaust gas aftertreatment component is an ASC.
  • a model of the diesel oxidation catalytic converter is part of a regulation of the SCR system, preferably the SCR injection.
  • the DOC itself is not regulated because it is a passive component.
  • the neural network preferably calculates a ratio between NO and NO2
  • the invention preferably relates to a system for controlling and / or regulating
  • Exhaust aftertreatment system of an internal combustion engine the system being a control device arranged in a vehicle for the
  • Method created simplified mathematical model includes.
  • the system comprises an artificial neural network whose behavior, in particular its transfer function, is set by training with a model of the at least one exhaust gas aftertreatment component, the model being a mathematical-physical mapping of the at least one exhaust gas aftertreatment component and in particular a kinetic model of the is at least one exhaust aftertreatment component.
  • At least one kinetic model in the system is replaced by a simplified mathematical model designed as an artificial neural network.
  • a simplified mathematical model can be used to replace a kinetic model of a diesel particulate filter, a diesel oxidation catalytic converter or an SCR catalytic converter.
  • the simplified mathematical model can be used to calculate at least one of the variables that determine the required amount of fuel to be injected into an SCR system.
  • the particle filter can optionally comprise a catalytic layer and be designed as a so-called SDPF.
  • the neural network is a
  • this test arrangement basically only has to include one or more neural networks and the kinetic model to be replaced and can therefore be executed on a conventional computer.
  • Different exhaust gas components such as NO, NO2 and O2, can be calculated in separate neural networks.
  • two hidden layers with six neurons or more per layer can be used per neural network.
  • a gas concentration calculation in particular a gas concentration calculation, can take place in or after a Diesel oxidation catalyst are carried out.
  • a diesel oxidation catalytic converter cannot be stored, which also simplifies the calculation of the gas concentration after the diesel oxidation catalytic converter.
  • the method according to the invention can also be used with storable elements such as, for example, an SCR catalytic converter or an ammonia slip catalytic converter or a DPF or an SDPF.
  • storable elements such as, for example, an SCR catalytic converter or an ammonia slip catalytic converter or a DPF or an SDPF.
  • a recurrent neural network can be used with such components.
  • the invention optionally relates to a method for creating a system for controlling and / or regulating at least one exhaust gas aftertreatment component of an exhaust gas aftertreatment system of an internal combustion engine, in which a simplified mathematical model is created using an artificial neural network, and this simplified mathematical model is subsequently a part of the system is to provide a control and / or regulation of the
  • the invention optionally relates to a method for controlling and / or regulating at least one exhaust gas aftertreatment component
  • Exhaust aftertreatment component is used.
  • the invention optionally relates to a system for controlling and / or regulating at least one exhaust gas aftertreatment component
  • real data from sensors can be used to train the neural network and to create the simplified mathematical model.
  • several vehicles with different drivers can be operated over a longer period of time under real conditions in order to obtain measurement data record, which can then be used to train the neural network.
  • Measured values such as measured values from the exhaust gas aftertreatment system, the cooling system, or other components, are linked.
  • a virtual driving profile can be generated in a first step, for example using a digital road map.
  • This driving data or the road profile can subsequently be used in an engine model or a vehicle model in order to record a virtual trip and engine data.
  • These engine data can then be fed to a kinetic model of the exhaust gas aftertreatment system in order to calculate parameters of the exhaust gas aftertreatment system.
  • the data obtained in this way can then be the input data and the output data of the neural network to be trained.
  • the simplified mathematical model is then created by the neural network, which is then used as part of the system for regulating and / or controlling the exhaust gas aftertreatment system.
  • FIG. 1 showing a possible schematic representation of a possible neural network
  • FIG. 2 showing details of a neural network that can be used.
  • a neural network 5 is used, which in the present embodiment is designed as a so-called feed-forward neural network, the neural network having two “hidden layers”, that is to say two hidden layers 2, 3, that is to say one “output layer” comprises an output layer 4 and an “input layer”, that is to say an input layer or a normalization layer 1.
  • FIG. 2 A possible structure of the relevant models is shown in FIG. 2.
  • Such a neural network can be used, for example, to replace a kinetic model in an engine control unit that is based on a physically mathematical mapping of an exhaust gas aftertreatment component, such as
  • a diesel oxidation catalyst for example a diesel oxidation catalyst corresponds.
  • the exhaust aftertreatment system includes along the
  • Diesel oxidation catalyst a NOx sensor can be arranged.
  • An injection nozzle for introducing the fuel for selective catalytic reduction is preferably arranged upstream of the SCR catalytic converter.
  • the operating material is usually a reducing agent or a substance that can be converted into a reducing agent such as
  • urea solution for example urea solution or a substance containing ammonia.
  • the model that calculates the processes in the diesel oxidation catalytic converter is set up to calculate the NO, NO2 and, in particular, the NOx gas concentration upstream of the SCR system, in particular upstream of the SCR catalytic converter. The calculated
  • the gas concentration can then be used in a conventional manner for metering the operating material for the SCR system.

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Toxicology (AREA)
  • Analytical Chemistry (AREA)
  • Combined Controls Of Internal Combustion Engines (AREA)
  • Exhaust Gas After Treatment (AREA)

Abstract

L'invention concerne un procédé et un système de création d'un système de commande et/ou de régulation d'au moins un composant de post-traitement de gaz d'échappement d'une installation de post-traitement des gaz d'échappement d'un moteur à combustion interne, le système comprenant un dispositif, disposé dans un véhicule, de commande de l'installation de post-traitement de gaz d'échappement, notamment le dispositif de gestion du moteur, le dispositif de commande destiné à commander et/ou réguler le composant de post-traitement de gaz d'échappement comprenant un modèle mathématique simplifié, le modèle mathématique simplifié effectuant un calcul de concentration de gaz en aval du composant de post-traitement de gaz d'échappement, le modèle mathématique simplifié étant créé à l'aide d'un réseau neuronal artificiel, les étapes suivantes étant réalisées : entrer des valeurs d'entrée dans un modèle cinétique du composant de post-traitement de gaz d'échappement et calculer des valeurs de sortie, en particulier des concentrations de gaz en aval du composant de post-traitement de gaz d'échappement, au moyen du modèle cinétique et effectuer l'apprentissage du réseau neuronal artificiel de sorte que, lors de l'entrée des mêmes valeurs d'entrée, il calcule sensiblement les mêmes valeurs de sortie que le modèle cinétique.
PCT/AT2020/060055 2019-03-01 2020-02-27 Procédé et système de commande et/ou de régulation d'au moins un composant de post-traitement de gaz d'echappement Ceased WO2020176914A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
DE112020001020.1T DE112020001020A5 (de) 2019-03-01 2020-02-27 Verfahren und System zur Steuerung und/oder Regelung mindestens einer Abgasnachbehandlungskomponente

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
ATA50166/2019 2019-03-01
ATA50166/2019A AT522231B1 (de) 2019-03-01 2019-03-01 Verfahren und System zur Steuerung und/oder Regelung mindestens einer Abgasnachbehandlungskomponente

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CN114991920A (zh) * 2022-05-23 2022-09-02 重庆文理学院 一种用于柴油机汽车尾气氮氧化物的处理系统
US20220316384A1 (en) * 2021-03-30 2022-10-06 Robert Bosch Gmbh Method and device for manipulation detection on a technical device in a motor vehicle with the aid of artificial intelligence methods
WO2022233602A1 (fr) 2021-05-05 2022-11-10 IFP Energies Nouvelles Procede de construction d'un modele de simulation d'une reaction chimique
WO2023279338A1 (fr) * 2021-07-08 2023-01-12 Shanghaitech University Reconstruction de champ spectral neuronal pour spectromètre
WO2024110415A1 (fr) 2022-11-21 2024-05-30 Basf Se Procédé de régulation des émissions d'une réaction chimique

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Cited By (7)

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Publication number Priority date Publication date Assignee Title
US20220316384A1 (en) * 2021-03-30 2022-10-06 Robert Bosch Gmbh Method and device for manipulation detection on a technical device in a motor vehicle with the aid of artificial intelligence methods
WO2022233602A1 (fr) 2021-05-05 2022-11-10 IFP Energies Nouvelles Procede de construction d'un modele de simulation d'une reaction chimique
FR3122767A1 (fr) 2021-05-05 2022-11-11 IFP Energies Nouvelles Procédé de construction d’un modèle de simulation d’une réaction chimique
WO2023279338A1 (fr) * 2021-07-08 2023-01-12 Shanghaitech University Reconstruction de champ spectral neuronal pour spectromètre
CN114991920A (zh) * 2022-05-23 2022-09-02 重庆文理学院 一种用于柴油机汽车尾气氮氧化物的处理系统
CN114991920B (zh) * 2022-05-23 2023-06-20 重庆文理学院 一种用于柴油机汽车尾气氮氧化物的处理系统
WO2024110415A1 (fr) 2022-11-21 2024-05-30 Basf Se Procédé de régulation des émissions d'une réaction chimique

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AT522231B1 (de) 2022-11-15
AT522231A1 (de) 2020-09-15

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