EP1703110A1 - Méthode d'optimisation de la calibration d'un moteur à explosion - Google Patents

Méthode d'optimisation de la calibration d'un moteur à explosion Download PDF

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Publication number
EP1703110A1
EP1703110A1 EP05102151A EP05102151A EP1703110A1 EP 1703110 A1 EP1703110 A1 EP 1703110A1 EP 05102151 A EP05102151 A EP 05102151A EP 05102151 A EP05102151 A EP 05102151A EP 1703110 A1 EP1703110 A1 EP 1703110A1
Authority
EP
European Patent Office
Prior art keywords
parameters
calibration
internal combustion
changed
engine
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.)
Withdrawn
Application number
EP05102151A
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German (de)
English (en)
Inventor
Frank Will
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.)
Ford Global Technologies LLC
Original Assignee
Ford Global Technologies LLC
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 Ford Global Technologies LLC filed Critical Ford Global Technologies LLC
Priority to EP05102151A priority Critical patent/EP1703110A1/fr
Publication of EP1703110A1 publication Critical patent/EP1703110A1/fr
Withdrawn legal-status Critical Current

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    • 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
    • F02D41/2432—Methods of calibration
    • 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
    • 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
    • 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
    • F02D41/1406—Introducing closed-loop corrections characterised by the control or regulation method with use of a optimisation method, e.g. iteration
    • 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
    • F02D41/2477—Methods of calibrating or learning characterised by the method used for learning

Definitions

  • the present invention relates to a method for optimizing the calibration of an internal combustion engine and in particular of an internal combustion engine for a motor vehicle.
  • the invention is described below essentially with reference to motor vehicles, the use is not limited to use on motor vehicles, but the invention can also find more application in the calibration of aircraft or marine engines and the like.
  • the internal combustion engine comprises a plurality of actuators which are actuated by a control unit in dependence on maps which contain actuating variables for the actuators for a plurality of operating points of the internal combustion engine.
  • a control unit in dependence on maps which contain actuating variables for the actuators for a plurality of operating points of the internal combustion engine.
  • APRBS amplitude-modulated pseudo-noise binary
  • a disadvantage of this known method is that there is no uniform distribution of the measuring points.
  • the duration and amplitude of the APRBS signals are generated by means of random number generators. That's why it's going to be a long period of time required to ensure that all possible parameter combinations of operating points are adequately covered.
  • the random definition of the measuring points influences the result of the system model.
  • the inventive method for optimizing the calibration of internal combustion engines is performed taking into account dynamic state changes of the engine using a neural network.
  • the calibration test starts from a start condition and for calibration, defined changes of the parameters are set.
  • the method according to the invention has many advantages.
  • the fact that the calibration test starts from a start condition ensures that reproducible conditions are present during the calibration. Since defined changes in the parameters are set, the calibration is not subject to random influences caused by the random selection of measuring points.
  • the quality of the system model is improved and, as a result, the results in terms of economy and emissions are improved while maintaining the performance of the internal combustion engine. Furthermore, the effort for the calibration is reduced, resulting in time and cost advantages.
  • the parameters are changed in a defined sequence.
  • the parameters are changed in a predefined order. It is also possible that in the order of the parameters to be changed and the Measurement results of the currently measured point to be taken into account by the variation of a certain parameter is performed more intense or by the variation of a parameter is omitted, for example, if the measurement results sufficiently below the statutory requirements and / or meet the internal requirements.
  • At least two of the parameters throttle position, load, fuel-air ratio, ignition angle, exhaust gas recirculation fraction, fuel quantity, injection pressure, Leitschaufelhorn a possibly existing turbocompressor with variable blade position, valve timing, injection timing and injection modulation in a defined order.
  • three, four, five or more of the aforementioned parameters are changed in a defined sequence. It is possible that at certain measuring points, the variation of one or more parameters is omitted.
  • At least two, preferably at least three of the parameters throttle valve position, load, fuel-air ratio, ignition angle and exhaust gas recirculation fraction are changed in this order.
  • one of the parameters is not varied, but is passed directly to the next parameter or even measuring point.
  • a parameter change is performed in both directions, so that first a parameter change in a first direction and then a change of the parameter in the opposite direction is performed.
  • Such a procedure is very advantageous because different temperature conditions and other hysteresis effects are taken into account due to the different directions of the parameter changes.
  • the measurement results of a dynamic engine condition at a particular throttle position may differ depending on whether the opening has been increased or decreased.
  • the start condition is set between parameter changes.
  • the start condition may be the idle state of the engine, but the start condition may also be varied during the calibration process, e.g. according to a predefined profile.
  • the neural network is trained online. Furthermore, it is preferred that the neural network itself determines the next parameter (s) for the following test.
  • a vehicle cooling system is used for engine cooling. This has considerable advantages, since the engine is subjected to the real cooling conditions.
  • the calibration is performed with a real load, while in conventional methods the speed and torque of the motor are kept constant while changing the parameters.
  • this embodiment of the method causes an increase in the throttle position also directly increasing the speed, as is the case in reality.
  • equidistant changes are made to the parameters during the calibration.
  • FIG. 1 is a highly schematic and exemplary representation of a structure of a system model of an internal combustion engine 1. At the current operating point of the internal combustion engine 1 different parameters act, which influence the exhaust emissions 11, the noise emissions 12, the current consumption 13 and the output power 14 of the engine.
  • the throttle position 2 acts on the rotational speed of the internal combustion engine 1.
  • An increase in the currently acting on the engine 1 load 3 will cause a reduction in the speed of the engine with otherwise unchanged parameters, while a reduction in the load usually causes an increase in speed.
  • the fuel-air ratio 4, the ignition angle 5, the exhaust gas recirculation fraction 6 and other parameters, not shown, such as the fuel quantity or the Leitschaufelhorn a possibly existing turbocompressor with variable blade position also affect the current operating condition of the engine 1, the respective Operating point corresponding exhaust emissions 11, noise emissions 12, a corresponding consumption 13 and a corresponding power output 14 has.
  • a premature termination of further parameter variations at a given measuring point is considered in particular if all the required values are already adhered to in the previously performed variations and if no further increase in quality can be expected by further variation of the parameters.
  • the change of a parameter namely here the ignition angle, over time is shown by way of example.
  • the parameter "ignition angle" is varied in both directions in order to take into account both temperature influences and hysteresis effects for the determination of the dynamic operating point.
  • a corresponding procedure is also possible and preferred in the case of the variation of other parameters.
  • a first firing angle 31 is set, which corresponds to the starting condition for this measuring point.
  • a firing angle 32 is set which is, for example, 10 °.
  • the period of time during which a specific firing angle is set can also be set to be shorter or longer.
  • a firing angle 33 is then set for also a period of 2 seconds so as to determine the transition from 10 ° to 12 ° firing angle.
  • the starting condition of the ignition angle can be set again at a time 24, or else it is directly advanced to the time 25, to which there is likewise an ignition angle 33 of 12 °.
  • an ignition angle 32 of 10 ° is set at time 26, and finally, at a time 27, the ignition angle 31 can again be set.
  • the firing angle is set to 14 ° at time 25 and 12 ° at time 26, so as to obtain the dynamic operating point with a firing angle of 12 ° both at approach of larger firing angles also detect smaller ignition angles.
  • a neural network is used, as is known from the generally known and in particular the cited prior art.
  • the neural network is trained online and can determine the parameter variations for the next operating point itself.

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Combined Controls Of Internal Combustion Engines (AREA)
EP05102151A 2005-03-18 2005-03-18 Méthode d'optimisation de la calibration d'un moteur à explosion Withdrawn EP1703110A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP05102151A EP1703110A1 (fr) 2005-03-18 2005-03-18 Méthode d'optimisation de la calibration d'un moteur à explosion

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP05102151A EP1703110A1 (fr) 2005-03-18 2005-03-18 Méthode d'optimisation de la calibration d'un moteur à explosion

Publications (1)

Publication Number Publication Date
EP1703110A1 true EP1703110A1 (fr) 2006-09-20

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EP05102151A Withdrawn EP1703110A1 (fr) 2005-03-18 2005-03-18 Méthode d'optimisation de la calibration d'un moteur à explosion

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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2906367A1 (fr) * 2006-09-27 2008-03-28 Peugeot Citroen Automobiles Sa Procede de modelisation d'un systeme complexe tel un moteur a combustion d'un vehicule automobile
EP2128408A3 (fr) * 2008-05-29 2011-04-20 Honeywell International Inc. Procédé de calibrage d'un système actionneur pour la buse variable d'un turbocompresseur
DE102015207252A1 (de) 2015-04-21 2016-10-27 Avl List Gmbh Verfahren und Vorrichtung zur modellbasierten Optimierung einer technischen Einrichtung
AT518174A4 (de) * 2016-02-17 2017-08-15 Avl List Gmbh Verfahren zur Verringerung der Schwankungsbreite der Abgasemissionswerte
DE102019216793A1 (de) 2018-10-31 2020-04-30 Avl List Gmbh Verfahren und System zur Simulation einer von einer Brennkraftmaschine emittierten Partikelanzahl
WO2020118330A1 (fr) * 2018-12-10 2020-06-18 Avl List Gmbh Procédé d'étalonnage d'un système technique
CN112282949A (zh) * 2020-09-23 2021-01-29 北汽福田汽车股份有限公司 电控汽油机起燃工况控制参数优化方法、装置以及车辆
DE102020102863A1 (de) 2020-02-05 2021-08-05 Festo Se & Co. Kg Parametrierung einer Komponente in der Automatisierungsanlage
CN114729608A (zh) * 2019-11-12 2022-07-08 Avl 里斯脱有限公司 用于校准机器控制器的方法和系统

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19741973C1 (de) 1997-09-23 1999-04-22 Daimler Chrysler Ag Verfahren zur Bestimmung der Rußkonzentration von selbstzündenden Brennkraftmaschinen
DE10020448A1 (de) 2000-04-26 2001-10-31 Daimler Chrysler Ag Verfahren und Vorrichtung zur Optimierung des Betriebs eines Verbrennungsmotors
EP1387067A2 (fr) * 2002-08-01 2004-02-04 Toyota Jidosha Kabushiki Kaisha Dispositif et méthode automatique de test de conformité, véhicule, et support de stockage

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19741973C1 (de) 1997-09-23 1999-04-22 Daimler Chrysler Ag Verfahren zur Bestimmung der Rußkonzentration von selbstzündenden Brennkraftmaschinen
DE10020448A1 (de) 2000-04-26 2001-10-31 Daimler Chrysler Ag Verfahren und Vorrichtung zur Optimierung des Betriebs eines Verbrennungsmotors
EP1387067A2 (fr) * 2002-08-01 2004-02-04 Toyota Jidosha Kabushiki Kaisha Dispositif et méthode automatique de test de conformité, véhicule, et support de stockage

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
CHRIS ATKINSON ET AL: "2005-01-0026 Dynamic Model-Based Calibration Optimization: An Introduction and Application to Diesel Engines SAE TECHNICAL PAPER SERIES", SAE WORLD CONGRESS DETROIT, 1 January 2005 (2005-01-01), pages 1 - 12, XP055260790 *
KNOEDLER K ET AL: "MODELLBASIERTE ONLINE-OPTIMIERUNG MODERNER VERBRENNUNGSMOTOREN TEIL 2: GRENZEN DES FAHRBAREN SUCHRAUMS", MTZ MOTORTECHNISCHE ZEITSCHRIFT, VIEWEG, WIESBADEN, DE, vol. 64, no. 6, June 2003 (2003-06-01), pages 520 - 526, XP001164053, ISSN: 0024-8525 *
MATTHIAS SCHÜLER, MICHAEL HAFNER, ROLF ISERMANN: "Einsatz schneller neuronaler Netzte zur modellbasierten Optimierung von Verbrennungsmotoren", MTZ MOTORTECHNISCHE ZEITSCHRIFT, no. 61, October 2000 (2000-10-01), pages 704 - 711, XP002341168 *

Cited By (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008037908A1 (fr) * 2006-09-27 2008-04-03 Peugeot Citroën Automobiles SA Procede de modelisation d'un systeme complexe tel un moteur a combustion d'un vehicule automobile
FR2906367A1 (fr) * 2006-09-27 2008-03-28 Peugeot Citroen Automobiles Sa Procede de modelisation d'un systeme complexe tel un moteur a combustion d'un vehicule automobile
EP2128408A3 (fr) * 2008-05-29 2011-04-20 Honeywell International Inc. Procédé de calibrage d'un système actionneur pour la buse variable d'un turbocompresseur
US11017132B2 (en) 2015-04-21 2021-05-25 Avl List Gmbh Method and device for model-based optimization of a technical device
DE102015207252A1 (de) 2015-04-21 2016-10-27 Avl List Gmbh Verfahren und Vorrichtung zur modellbasierten Optimierung einer technischen Einrichtung
WO2016170063A1 (fr) 2015-04-21 2016-10-27 Avl List Gmbh Procédé et dispositif d'optimisation d'un dispositif technique sur la base d'un modèle
CN107849998A (zh) * 2015-04-21 2018-03-27 Avl 里斯脱有限公司 用于基于模型地优化工程装置的方法和设备
AT518174A4 (de) * 2016-02-17 2017-08-15 Avl List Gmbh Verfahren zur Verringerung der Schwankungsbreite der Abgasemissionswerte
AT518174B1 (de) * 2016-02-17 2017-08-15 Avl List Gmbh Verfahren zur Verringerung der Schwankungsbreite der Abgasemissionswerte
DE102019216793A1 (de) 2018-10-31 2020-04-30 Avl List Gmbh Verfahren und System zur Simulation einer von einer Brennkraftmaschine emittierten Partikelanzahl
AT521865A1 (de) * 2018-10-31 2020-05-15 Avl List Gmbh Verfahren und System zur Simulation einer von einer Brennkraftmaschine emittierten Partikelanzahl
AT521865B1 (de) * 2018-10-31 2020-10-15 Avl List Gmbh Verfahren und System zur Simulation einer von einer Brennkraftmaschine emittierten Partikelanzahl
WO2020118330A1 (fr) * 2018-12-10 2020-06-18 Avl List Gmbh Procédé d'étalonnage d'un système technique
US11899414B2 (en) 2018-12-10 2024-02-13 Avl List Gmbh Method for calibrating a technical system
CN114729608A (zh) * 2019-11-12 2022-07-08 Avl 里斯脱有限公司 用于校准机器控制器的方法和系统
CN114729608B (zh) * 2019-11-12 2024-03-08 Avl 里斯脱有限公司 用于校准机器控制器的方法和系统
DE102020102863A1 (de) 2020-02-05 2021-08-05 Festo Se & Co. Kg Parametrierung einer Komponente in der Automatisierungsanlage
CN113219934A (zh) * 2020-02-05 2021-08-06 费斯托股份有限两合公司 自动化系统中的部件的参数设定
US11960251B2 (en) 2020-02-05 2024-04-16 Festo Se & Co. Kg Parameterization of a component in an automation system
CN112282949A (zh) * 2020-09-23 2021-01-29 北汽福田汽车股份有限公司 电控汽油机起燃工况控制参数优化方法、装置以及车辆
CN112282949B (zh) * 2020-09-23 2021-07-16 北汽福田汽车股份有限公司 电控汽油机起燃工况控制参数优化方法、装置以及车辆

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