EP2195519B1 - Schätzung der statusparameter eines motors mit messung des innendrucks eines zylinders - Google Patents

Schätzung der statusparameter eines motors mit messung des innendrucks eines zylinders Download PDF

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Publication number
EP2195519B1
EP2195519B1 EP08837795A EP08837795A EP2195519B1 EP 2195519 B1 EP2195519 B1 EP 2195519B1 EP 08837795 A EP08837795 A EP 08837795A EP 08837795 A EP08837795 A EP 08837795A EP 2195519 B1 EP2195519 B1 EP 2195519B1
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European Patent Office
Prior art keywords
cylinder
variables
learning model
engine
internal pressure
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EP08837795A
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English (en)
French (fr)
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EP2195519A1 (de
Inventor
Sébastien CASTRIC
Vincent Talon
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Renault SAS
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Renault SAS
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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
    • F02D35/00—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for
    • F02D35/02—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for on interior conditions
    • F02D35/023—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for on interior conditions by determining the cylinder pressure
    • 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
    • F02D41/24—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means
    • F02D41/2406—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means using essentially read only memories
    • F02D41/2425—Particular ways of programming the data
    • F02D41/2429—Methods of calibrating or learning
    • F02D41/2451—Methods of calibrating or learning characterised by what is learned or calibrated
    • 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
    • F02D2200/10—Parameters related to the engine output, e.g. engine torque or engine speed
    • F02D2200/1012—Engine speed gradient
    • 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
    • F02D41/24—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 invention relates to the estimation of state parameters of a rotary internal combustion engine comprising a plurality of cylinders.
  • the object of the invention is therefore to provide a system for controlling the state parameters of an engine making it possible to respond to the needs mentioned above and, in particular, to propose a system for estimating these state parameters which enables to remove some sensors such as that of the pressure at the cylinder outlet or upstream of the turbine of a turbocharger P avt or the temperature upstream of the turbine T avt .
  • Another object of the invention is to provide a system for estimating the state parameters of an engine which makes it possible to to overcome the preliminary design of many engine tuning maps and thus significantly reduce the development time.
  • the system estimates at least one state parameter of an internal combustion engine comprising: at least one cylinder and a movable piston driven through a crankshaft; means for measuring the time variation of the crankshaft angle and the internal pressure of said cylinder; at least one physical model for calculating a plurality of intermediate time variables from said measurements of the crankshaft angle and the cylinder internal pressure and from a measurement of at least one engine condition parameter; means for creating time variable tables discretized from said intermediate time variables; and a learning model for estimating at least one engine state parameter from said discrete time variable tables.
  • the learning model can be for example, neural network type, statistical type or type of kriging.
  • the measurement of the internal pressure of the cylinder can be carried out by means of a pressure sensor.
  • a pressure sensor may be equipped with such a pressure sensor or, more simply, only one of the engine cylinders.
  • the system comprises means for initializing the learning model by performing prior tests.
  • the internal combustion engine 2 comprises a cylinder 3 in which a piston 4 moves by means of a connecting rod 5 connecting the piston 4 to the crankshaft 6.
  • a combustion chamber 7 is delimited by said cylinder 3, said piston 4 and a cylinder head 8.
  • the cylinder head 8 is provided with at least two valves 9 and 10 which make it possible to connect the combustion chamber 7 with respectively the intake manifold 9a, for air possibly mixed with a part of the gases of exhaust, and the exhaust manifold of gases 10a.
  • the engine 2 also comprises a fuel injector 11 arranged to inject fuel into the combustion chamber 7.
  • the estimation system comprises two measurement sensors 20 and 21 as well as an electronic calculation unit 22 comprising three modules: a calculation module 23, a discretization module 24 and an estimation module 25.
  • the sensor 20 makes it possible to measure at any instant the angle ⁇ of the crankshaft 6, the sensor 21 makes it possible to measure the internal pressure P cyl of the cylinder 3 which corresponds to the pressure inside the combustion chamber 7.
  • These sensors 20 and 21 each emit a temporal measurement signal, transmitted respectively by the connections 20a and 21a, in the direction of the electronic calculation unit 22.
  • the calculation module 23 comprises several physical models 231 to 237 which make it possible to calculate a certain number of intermediate time variables from the input time signals ⁇ , P cyl and from the measurements of certain state parameters of the engine 2, brought by the connection 22a to the input of the calculation module 23.
  • the intermediate temporal variables are transmitted by connections 26 to the input of the discretization module 24.
  • the intermediate temporal variables may be, for example, the temperature of the cylinder T cyl , the heat release Q , the mass fraction of gas burned X b , the mass of liquid fuel M carb_liq and vaporized M carb_vap , the mass of fresh gas M gf and burnt gas M gb , the rate of burned gas X gb , or the polytropic coefficient k .
  • the state parameters of the motor 2 brought by the connection 22a are, for example, parameters such as the engine speed, the fuel injection timing ⁇ inj or the mass of fuel introduced for each injection M inj . These are variables distinct from the calculated intermediate temporal variables.
  • the intermediate temporal variables are discretized in the module 24 to generate tables of discrete temporal variables. This discretization of the signals takes place at precise instants for certain measurements of angles ⁇ of the crankshaft 6.
  • the estimation module 25 receives these tables of temporal variables discretized by the connections 27 in order to estimate the desired state parameters, such as, for example, the filling ⁇ v or the temperature at the outlet of the cylinder T avt .
  • the internal pressure P cyl of the cylinder 3 thus makes it possible to construct intermediate temporal variables in order to derive certain state parameters from the engine 2.
  • This construction of the temporal variables is carried out by means of models 231 to 237 which are based only on temporal variables, excluding any space variable.
  • the models 231 to 237 receive as inputs the variables P cyl , ⁇ and certain state parameters brought by the connection 22a. It is also possible that a physical model can use as input a plurality of intermediate temporal variables, brought by the connections (30), which are the result of a calculation made by another model, thus increasing the number of computation combinations. intermediate variables.
  • Physical model 231 Calculation of the cylinder temperature in the combustion chamber T cyl .
  • the total mass M t can be determined by mapping according to the engine speed and the pressure of the intake manifold 9a.
  • variable T cyl thus calculated is transmitted directly to the discretization module 24.
  • the Q variable thus calculated is transmitted as input to the physical models 233, 234 and 235, as well as to the discretization module 24.
  • Model 233 calculation of the mass fraction of burnt gases X b .
  • X b The mass fraction of burnt gases X b evolves during combustion.
  • An image of X b can be obtained through the release of heat Q. Indeed the heat released is proportional to the mass of fuel burned. So the integral heat Q is directly related to the mass of fuel already burned. This integral is normalized between 0 and 1. It then represents the evolution of combustion. It is called X b .
  • X b Norm ⁇ ⁇ d Q d ⁇ ⁇ ⁇ ⁇ dt ⁇ Where ⁇ is the angular velocity of the motor in radians per second.
  • variable X b thus calculated is transmitted directly to the discretization module 24.
  • Model 234 Calculation of the mass of liquid fuel M carb_liq and vaporized M carb vap .
  • the average flow rate of combustion being directly proportional to the heat release Q , it can be calculated from the previous physical model 232.
  • the mass of liquid and vaporized fuel present in the combustion chamber 7 can thus be known.
  • Model 235 calculation of the mass of fresh gas M gf and of burnt gas M gb .
  • the flue gases have two origins: one part (called EGR) is the partially recycled exhaust gases from the exhaust manifold 10a to the intake manifold 9a, another part (called GBR) are the residual gases of the preceding cycle which have not been drained.
  • EGR the partially recycled exhaust gases from the exhaust manifold 10a to the intake manifold 9a
  • GBR the residual gases of the preceding cycle which have not been drained.
  • the variables M gf and M gb thus calculated are transmitted as inputs to the physical model 236, as well as to the discretization module 24.
  • Model 236 calculation of the rate of burned gases X gb Thinner used to reduce NO x .
  • This rate of burned gas X gb is the proportion of flue gas present at the closure of the intake valve 9 with respect to the total mass M t enclosed in the cylinder 3.
  • X gb M gb
  • M t M gb M gf + M gb
  • variable X gb thus calculated is transmitted directly to the discretization module 24.
  • Model 237 calculation of the polytropic coefficient in compression phase k .
  • This calculation interval ⁇ may correspond at least to a sampling step of the signal of the internal pressure of the cylinder P cyl as a function of the angle ⁇ of the crankshaft 6. In general, the interval is taken from the order of 10 no sampling of said signal.
  • variable k thus calculated is transmitted directly to the discretization module 24.
  • This module 25 comprises learning models 28 which can be of the neural network type, as illustrated in the figure, or statistics or of the kriging type.
  • kriging models which are interpolation models using stochastic methods that allow a calculation of probabilities applied to the processing of statistical data.
  • an ordinary kriging will be used.
  • kriging is based on the correlation between the variables that one wishes to estimate and the discretized variables that are the inputs of the model.
  • the x i are the tables of discretized variables obtained by the module 24, the y (x i ) are the values of the variables which one wishes to estimate, as for example the filling ⁇ v .
  • the principle of kriging is to determine the coefficients ⁇ i , which are dependent on x i , by studying the degree of similarity between y (x i ) from the covariance between points x i as a function of the distance between these points. .
  • the weights ⁇ i associated with each of the values y (x i ) are chosen so as to obtain a prediction ⁇ of minimum variance.
  • the learning models 28 are therefore previously identified on tests carried out on the engine test bench or on a vehicle.

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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)

Claims (10)

  1. System zur Schätzung mindestens eines Zustandsparameters eines Verbrennungsmotors (2), der mindestens einen Zylinder (3), einen mittels einer Kurbelwelle (6) angetriebenen beweglichen Kolben (4), Einrichtungen (20, 21) zur Messung der zeitlichen Veränderung des Winkels der Kurbelwelle und des Innendrucks des Zylinders enthält, dadurch gekennzeichnet, dass es enthält: mindestens ein physikalisches Modell (231) zur Berechnung einer Vielzahl von zeitlichen Zwischenvariablen ausgehend von den Messungen des Winkels der Kurbelwelle und des Innendrucks des Zylinders und ausgehend von einer Messung mindestens eines Zustandsparameters des Motors; eine Einrichtung (24) zur Erzeugung von Tabellen von diskretisierten zeitlichen Variablen ausgehend von den zeitlichen Zwischenvariablen; und ein Lernmodell (28), um mindestens einen Zustandsparameter des Motors (2) ausgehend von den Tabellen diskretisierter zeitlicher Variablen zu schätzen.
  2. System nach Anspruch 1, das ein Lernmodell (28) von der Art neuronales Netz enthält.
  3. System nach einem der Ansprüche 1 bis 2, das ein Lernmodell (28) der statistischen Art enthält.
  4. System nach einem der Ansprüche 1 bis 3, das ein Lernmodell (28) der Art Kriging enthält.
  5. System nach einem der Ansprüche 1 bis 4, das eine Einrichtung zur Initialisierung des Lernmodells (28) mittels Durchführung vorheriger Versuche enthält.
  6. Verfahren zur Schätzung mindestens eines Zustandsparameters eines Verbrennungsmotors (2), der mindestens einen Zylinder (3), einen mittels einer Kurbelwelle (6) angetriebenen beweglichen Kolben (4) enthält, das einen ersten Schritt der Messung der zeitlichen Veränderung des Winkels der Kurbelwelle und des Innendrucks des Zylinders enthält, dadurch gekennzeichnet, dass es enthält:
    einen zweiten Schritt der Berechnung, mittels mindestens eines physikalischen Modells (231), einer Vielzahl von zeitlichen Zwischenvariablen ausgehend von den Messungen des Winkels der Kurbelwelle und des Innendrucks des Zylinders und ausgehend von einer Messung mindestens eines Zustandsparameters des Motors; einen dritten Schritt der Diskretisierung der zeitlichen Zwischenvariablen, der zur Erzeugung von Tabellen diskretisierter zeitlicher Variablen bestimmt ist;
    und einen vierten Schritt der Schätzung, mittels eines Lernmodells (28), mindestens eines Zustandsparameters des Motors (2) ausgehend von den Tabellen diskretisierter zeitlicher Variablen.
  7. Verfahren nach Anspruch 6, bei dem der Schätzschritt die Verwendung eines Lernmodells (28) von der Art neuronales Netz enthält.
  8. Verfahren nach einem der Ansprüche 6 und 7, bei dem der Schätzschritt die Verwendung eines Lernmodells (28) der statistischen Art enthält.
  9. Verfahren nach einem der Ansprüche 6 bis 8, bei dem der Schätzschritt die Verwendung eines Lernmodells (28) der Art Kriging enthält.
  10. Verfahren nach einem der Ansprüche 6 bis 9, bei dem der Schätzschritt einen Schritt der Initialisierung des Lernmodells (28) mittels Durchführung vorheriger Versuche enthält.
EP08837795A 2007-10-12 2008-08-19 Schätzung der statusparameter eines motors mit messung des innendrucks eines zylinders Active EP2195519B1 (de)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
FR0758268A FR2922262B1 (fr) 2007-10-12 2007-10-12 Estimation de parametres d'etat d'un moteur par mesure de la pression interne d'un cylindre
PCT/FR2008/051510 WO2009047412A1 (fr) 2007-10-12 2008-08-19 Estimation de parametres d'etat d'un moteur par mesure de la pression interne d'un cylindre

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EP2195519A1 EP2195519A1 (de) 2010-06-16
EP2195519B1 true EP2195519B1 (de) 2012-10-03

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FR (1) FR2922262B1 (de)
WO (1) WO2009047412A1 (de)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023220794A1 (pt) * 2022-05-16 2023-11-23 Robert Bosch Limitada Método para rastreamento de emissões de gases de efeito estufa

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2936015A1 (fr) * 2008-09-16 2010-03-19 Renault Sas Estimation de variables d'etat d'un moteur a combustion interne.
FR3012526B1 (fr) * 2013-10-24 2015-10-30 Renault Sas Systeme et procede d'estimation du debit d'oxydes d'azotes dans les gaz d'echappement d'un moteur a combustion interne pour vehicule automobile.
CN105488246B (zh) * 2015-11-13 2018-08-10 哈尔滨理工大学 一种静压支承转台速度对热油携带影响的数值计算方法
FR3044717B1 (fr) 2015-12-04 2017-11-24 Renault Sas Procede d'estimation de masse enfermee dans la chambre de combustion d'un cylindre d'un moteur a combustion interne de vehicule automobile

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10214833A1 (de) * 2002-04-04 2003-10-16 Volkswagen Ag Verfahren zum Bestimmen eines indizierten Ist-Motormomentes einer Brennkraftmaschine
US6935313B2 (en) * 2002-05-15 2005-08-30 Caterpillar Inc System and method for diagnosing and calibrating internal combustion engines
DE102004055313B4 (de) * 2004-11-16 2017-06-22 Volkswagen Ag Verfahren und Vorrichtung zur Diagnose oder Verstärkungsadaption von Zylinderdrucksensoren
US7299123B2 (en) * 2005-03-04 2007-11-20 Stmicroelectronics S.R.L. Method and device for estimating the inlet air flow in a combustion chamber of a cylinder of an internal combustion engine
DE102006008062B3 (de) * 2006-02-21 2007-05-10 Siemens Ag Motorsteuerung und Verfahren zur Bestimmung des Drucks in einem Brennraum einer Brennkraftmaschine

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023220794A1 (pt) * 2022-05-16 2023-11-23 Robert Bosch Limitada Método para rastreamento de emissões de gases de efeito estufa

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Publication number Publication date
FR2922262A1 (fr) 2009-04-17
FR2922262B1 (fr) 2010-03-12
EP2195519A1 (de) 2010-06-16
WO2009047412A1 (fr) 2009-04-16

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