EP1041264A2 - Modèle hybride pour modeler un procédé complet dans un véhicule - Google Patents

Modèle hybride pour modeler un procédé complet dans un véhicule Download PDF

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Publication number
EP1041264A2
EP1041264A2 EP00106509A EP00106509A EP1041264A2 EP 1041264 A2 EP1041264 A2 EP 1041264A2 EP 00106509 A EP00106509 A EP 00106509A EP 00106509 A EP00106509 A EP 00106509A EP 1041264 A2 EP1041264 A2 EP 1041264A2
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EP
European Patent Office
Prior art keywords
model
physical
hybrid
neural
simulated
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
EP00106509A
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German (de)
English (en)
Other versions
EP1041264A3 (fr
Inventor
Heiko Dr. Konrad
Gerd Krämer
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
Publication of EP1041264A2 publication Critical patent/EP1041264A2/fr
Publication of EP1041264A3 publication Critical patent/EP1041264A3/fr
Ceased legal-status Critical Current

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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01L—CYCLICALLY OPERATING VALVES FOR MACHINES OR ENGINES
    • F01L1/00—Valve-gear or valve arrangements, e.g. lift-valve gear
    • F01L1/34—Valve-gear or valve arrangements, e.g. lift-valve gear characterised by the provision of means for changing the timing of the valves without changing the duration of opening and without affecting the magnitude of the valve lift
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01L—CYCLICALLY OPERATING VALVES FOR MACHINES OR ENGINES
    • F01L9/00—Valve-gear or valve arrangements actuated non-mechanically
    • F01L9/20—Valve-gear or valve arrangements actuated non-mechanically by electric means
    • 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
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01L—CYCLICALLY OPERATING VALVES FOR MACHINES OR ENGINES
    • F01L2800/00—Methods of operation using a variable valve timing mechanism
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D13/00—Controlling the engine output power by varying inlet or exhaust valve operating characteristics, e.g. timing
    • F02D13/02—Controlling the engine output power by varying inlet or exhaust valve operating characteristics, e.g. timing during engine operation
    • F02D13/0203—Variable control of intake and exhaust valves
    • F02D13/0215—Variable control of intake and exhaust valves changing the valve timing only
    • 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
    • F02D41/0002—Controlling intake air
    • F02D2041/001—Controlling intake air for engines with variable valve actuation
    • 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
    • F02D2041/1433—Introducing closed-loop corrections characterised by the control or regulation method using a model or simulation of the system
    • F02D2041/1436—Hybrid model
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D2200/00—Input parameters for engine control
    • F02D2200/02—Input parameters for engine control the parameters being related to the engine
    • F02D2200/04—Engine intake system parameters
    • F02D2200/0402—Engine intake system parameters the parameter being determined by using a model of the engine intake or its components

Definitions

  • the invention relates to a hybrid model for modeling an overall process in a vehicle consisting of at least one physical and one neural sub-model.
  • the filling of cylinders in engines with variable valve train measured with a very delayed air mass sensor. It will therefore expediently from different input variables, which are directly at the inlet be measured and determined with the help of a model.
  • the Filling of the individual cylinders influenced by several manipulated variables, some of them are interdependent or independent.
  • Empirical methods such as Maps.
  • empirical methods are usually imprecise and require a high level Coordination effort.
  • Another possibility are physical functions, at which the process behavior from the consideration of the physical relationships is derived.
  • physical functions are sometimes difficult to create.
  • the overall system and the Dependencies to be known within the system.
  • the effort for the creation of physical models with increasing model complexity disproportionately too.
  • different concepts e.g. Direct injection, electronic valve train, variable valve train, etc.
  • DE 197 06 750 A1 describes a method for controlling the mixture in a Internal combustion engine and a device for performing this method known.
  • the Combustion chamber of the internal combustion engine air mass coming from a Input size determined.
  • the amount of fuel to be supplied in Determined as a function of this input variable.
  • the neural network is used to describe the Control variable for the fuel path depending on the engine operating state and the driver-influenced control variable for the air path.
  • the control variable for the fuel path is exclusive in this embodiment set on the neural network.
  • neural networks are outside the Work area in which the training data are determined, an implausible Can have extrapolation behavior and therefore in safety-critical Processes, e.g. in motor vehicles, are difficult to use.
  • the object of the present invention is to develop a hybrid model for modeling a To specify the overall process in a vehicle, with which physical have difficult to describe processes modeled without the implausible Extrapolation behavior must be accepted.
  • the overall process (for example the filling of the Cylinder) is divided into sub-processes, which are of different sub-models described and then combined into an overall model.
  • the neural model takes over the description of a process part, which is physical is difficult to grasp.
  • the modeling of the air mass filling can be used as a concrete application Specify internal combustion engines, for example with variable valve train. At this Application could determine the basic filling using a physical model become. However, the influence of camshaft spreading could neural network are described. Especially when describing the Influence of camshaft spreading is only possible with a high physical model Create effort.
  • the modeling of the basic model with a physical process description has the advantage that the share of the neural sub-model in the overall model can be deliberately restricted. This ensures that Overall model shows no implausible extrapolation behavior.
  • the merging of the different sub-models can be additive, for example and / or multiplicative.
  • the use of others is also logical or arithmetic links when the Results of the sub-models possible.
  • neural sub-model neural network
  • Continuous adaptation of the network parameters is also optional possible during the operation of the vehicle. For example Series tolerances are recorded and included.
  • hybrid models presented can also be used for other concepts can be reused by, for example, the input quantities of the neural Network can be relearned.
  • both the tax times can be included an electronic valve train and the spread in a motor with variable Model the valve train with the hybrid model presented.
  • Physical models sometimes use different maps or Characteristic curves that usually require a large amount of memory. In particular in the case of complicated processes, physical modeling is a big one Number of maps and characteristic curves required. In the present Overall, the use of a physical-neuronal hybrid model is less Storage space is required because the neural networks require elaborate maps and Characteristic curves can be avoided. Rather, the lesser need Network parameters in neural networks require less memory.
  • the only drawing shows a simple schematic block diagram in which an overall model for modeling the air mass filling at one Internal combustion engine with variable valve timing with a physical model for basic filling and a neural network model for the influence of spreading is described.
  • the basic filling is physical and depending on the speed N, the cylinder stroke (stroke) and the pressure difference D_P and the Suction temperature T_Ans described. These parameters are the physical model as input variables and determine accordingly a map stored in it and some thermodynamic Basic equations the initial quantity of the physical model.
  • the influence of the camshaft spread is determined using the neural network model described, since it is difficult to create a physical model here.
  • input variables for the neural network model serve (Stroke) the spreads of the intake and exhaust valves (E_Spr, A_Spr).
  • E_Spr, A_Spr the spreads of the intake and exhaust valves
  • Cylinder filling are determined and output.
  • This influence becomes multiplicative coupled with the output from the physical model, which leads to the then total determined air mass ML_Mod leads.
  • the proportion of the neuronal Partial model limited to the overall model. In the present case, the restriction is given in Dependence on the initial value of the physical sub-model.
  • a hybrid model can also be used to describe other overall processes such as an electronic valve train, turbocharged engines, direct injection engines or a synchronization control can be used, whereby each Sub-processes describe their own mostly completed processes and at least one sub-process is represented with a neural network.

Landscapes

  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Combined Controls Of Internal Combustion Engines (AREA)
  • Output Control And Ontrol Of Special Type Engine (AREA)
  • Feedback Control In General (AREA)
EP00106509A 1999-04-01 2000-03-25 Modèle hybride pour modeler un procédé complet dans un véhicule Ceased EP1041264A3 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE19914910A DE19914910A1 (de) 1999-04-01 1999-04-01 Hybridmodell zur Modellierung eines Gesamtprozesses in einem Fahrzeug
DE19914910 1999-04-01

Publications (2)

Publication Number Publication Date
EP1041264A2 true EP1041264A2 (fr) 2000-10-04
EP1041264A3 EP1041264A3 (fr) 2002-08-07

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EP00106509A Ceased EP1041264A3 (fr) 1999-04-01 2000-03-25 Modèle hybride pour modeler un procédé complet dans un véhicule

Country Status (2)

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EP (1) EP1041264A3 (fr)
DE (1) DE19914910A1 (fr)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1253491A2 (fr) * 2001-04-24 2002-10-30 Bayer Aktiengesellschaft Modèle hybride et procédé de détermination des propriétés mecaniques et des propriétés de traitement d' un article moulé par injection
EP1342899A1 (fr) * 2000-12-12 2003-09-10 Toyota Jidosha Kabushiki Kaisha Commande de moteur a combustion interne
WO2006000474A1 (fr) * 2004-06-24 2006-01-05 Siemens Aktiengesellschaft Procede pour determiner la masse d'air presente dans un cylindre
FR2876152A1 (fr) * 2004-10-06 2006-04-07 Renault Sas Procede et systeme ameliores d'estimation d'une temperature des gaz d'echappement et moteur a combustion interne equipe d'un tel systeme
DE102004055313A1 (de) * 2004-11-16 2006-05-18 Volkswagen Ag Verfahren und Vorrichtung zur Diagnose oder Verstärkungsadaption von Zylinderdrucksensoren
WO2006114550A1 (fr) * 2005-04-28 2006-11-02 Renault S.A.S Procede de commande d'un moteur de vehicule mettant en œuvre un reseau de neurones

Families Citing this family (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10113538B4 (de) * 2001-03-20 2012-03-01 Bayerische Motoren Werke Aktiengesellschaft Regelvorrichtung und Regelverfahren
DE10203919A1 (de) * 2002-01-31 2003-08-21 Bayerische Motoren Werke Ag Verfahren zur Rekonstruktion messbarer Grössen an einem System mit einer Brennkraftmaschine
DE10237328B4 (de) * 2002-08-14 2006-05-24 Siemens Ag Verfahren zum Regeln des Verbrennungsprozesses einer HCCI-Brennkraftmaschine
AT6293U1 (de) * 2002-12-05 2003-07-25 Avl List Gmbh Verfahren zur regelung bzw. steuerung einer in einem kreisprozess arbeitenden brennkraftmaschine
DE10328015A1 (de) * 2003-06-23 2005-01-13 Volkswagen Ag Virtuelle Lambdasonde für ein Kraftfahrzeug
DE102014000397A1 (de) 2014-01-17 2015-07-23 Fev Gmbh Modellbasierte Zylinderfüllungserfassung für eine Brennkraftmaschine
DE102021204544A1 (de) 2021-05-05 2022-11-10 Robert Bosch Gesellschaft mit beschränkter Haftung Verfahren zum Betreiben eines hydraulischen Zylinders einer Arbeitsmaschine
DE102022212907A1 (de) 2022-11-30 2024-06-06 Rheinisch-Westfälische Technische Hochschule Aachen, Körperschaft des öffentlichen Rechts Computerimplementiertes Verfahren und Vorrichtung zur Vorhersage eines Zustandes eines technischen Systems

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0445555A2 (fr) * 1990-03-06 1991-09-11 Bayerische Motoren Werke Aktiengesellschaft Procédé de régulation continue de phase d'arbre à cames en fonction du régime
DE19706756A1 (de) 1997-02-20 1998-09-03 Siemens Ag Gradientenverstärker für einen Kernspintomographen und Kernstpintomograph

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DE4338607B4 (de) * 1993-11-11 2005-10-06 Siemens Ag Verfahren und Vorrichtung zur Führung eines Prozesses in einem geregelten System
DE19547496C2 (de) * 1995-12-19 2003-04-17 Dierk Schroeder Verfahren zur Regelung von Verbrennungsmotoren
US5877954A (en) * 1996-05-03 1999-03-02 Aspen Technology, Inc. Hybrid linear-neural network process control
JPH10122017A (ja) * 1996-10-14 1998-05-12 Yamaha Motor Co Ltd エンジン制御方式
US5714683A (en) * 1996-12-02 1998-02-03 General Motors Corporation Internal combustion engine intake port flow determination
DE19706750A1 (de) * 1997-02-20 1998-08-27 Schroeder Dierk Prof Dr Ing Dr Verfahren zur Gemischsteuerung bei einem Verbrennungsmotor sowie Vorrichtung zu dessen Durchführung
DE19709955C2 (de) * 1997-03-11 2003-10-02 Siemens Ag Verfahren und Einrichtung zum Steuern einer Brennkraftmaschine

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0445555A2 (fr) * 1990-03-06 1991-09-11 Bayerische Motoren Werke Aktiengesellschaft Procédé de régulation continue de phase d'arbre à cames en fonction du régime
DE19706756A1 (de) 1997-02-20 1998-09-03 Siemens Ag Gradientenverstärker für einen Kernspintomographen und Kernstpintomograph

Cited By (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1342899A1 (fr) * 2000-12-12 2003-09-10 Toyota Jidosha Kabushiki Kaisha Commande de moteur a combustion interne
EP1342899A4 (fr) * 2000-12-12 2012-04-25 Toyota Motor Co Ltd Commande de moteur a combustion interne
EP2570637A3 (fr) * 2000-12-12 2014-07-23 Toyota Jidosha Kabushiki Kaisha Appareil de command pour un moteur à combustion interne à commande de soupape variable
EP2527631A3 (fr) * 2000-12-12 2014-08-27 Toyota Jidosha Kabushiki Kaisha Appareil de commande pour un moteur à combustion interne à commande de soupape variable
EP2527630A3 (fr) * 2000-12-12 2014-07-23 Toyota Jidosha Kabushiki Kaisha Appareil de commande pour un moteur à combustion interne à commande de soupape variable
EP1253491A2 (fr) * 2001-04-24 2002-10-30 Bayer Aktiengesellschaft Modèle hybride et procédé de détermination des propriétés mecaniques et des propriétés de traitement d' un article moulé par injection
EP1253491B1 (fr) * 2001-04-24 2006-08-02 Bayer MaterialScience AG Modèle hybride et procédé de détermination des propriétés mecaniques et des propriétés de traitement d' un article moulé par injection
US7357127B2 (en) 2004-06-24 2008-04-15 Siemens Aktiengesellschaft Method for determining the air mass in a cylinder
WO2006000474A1 (fr) * 2004-06-24 2006-01-05 Siemens Aktiengesellschaft Procede pour determiner la masse d'air presente dans un cylindre
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
WO2006037926A1 (fr) * 2004-10-06 2006-04-13 Renault S.A.S Procede et systeme ameliores d'estimation d'une temperature des gaz d'echappement et moteur a combustion interne equipe d'un tel systeme
FR2876152A1 (fr) * 2004-10-06 2006-04-07 Renault Sas Procede et systeme ameliores d'estimation d'une temperature des gaz d'echappement et moteur a combustion interne equipe d'un tel systeme
DE102004055313B4 (de) * 2004-11-16 2017-06-22 Volkswagen Ag Verfahren und Vorrichtung zur Diagnose oder Verstärkungsadaption von Zylinderdrucksensoren
DE102004055313A1 (de) * 2004-11-16 2006-05-18 Volkswagen Ag Verfahren und Vorrichtung zur Diagnose oder Verstärkungsadaption von Zylinderdrucksensoren
FR2885175A1 (fr) * 2005-04-28 2006-11-03 Renault Sas Procede de commande d'un moteur de vehicule mettant en oeuvre un reseau de neurones
CN101198783B (zh) * 2005-04-28 2010-10-13 雷诺股份公司 使用神经网络控制车辆发动机的方法
US7774127B2 (en) 2005-04-28 2010-08-10 Renault S.A.S. Method for controlling a motor vehicle using a network of neurones
WO2006114550A1 (fr) * 2005-04-28 2006-11-02 Renault S.A.S Procede de commande d'un moteur de vehicule mettant en œuvre un reseau de neurones

Also Published As

Publication number Publication date
DE19914910A1 (de) 2000-10-26
EP1041264A3 (fr) 2002-08-07

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