EP2195519A1 - Estimation de parametres d'etat d'un moteur par mesure de la pression interne d'un cylindre - Google Patents
Estimation de parametres d'etat d'un moteur par mesure de la pression interne d'un cylindreInfo
- Publication number
- EP2195519A1 EP2195519A1 EP08837795A EP08837795A EP2195519A1 EP 2195519 A1 EP2195519 A1 EP 2195519A1 EP 08837795 A EP08837795 A EP 08837795A EP 08837795 A EP08837795 A EP 08837795A EP 2195519 A1 EP2195519 A1 EP 2195519A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- cylinder
- learning model
- variables
- engine
- state parameter
- 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.)
- Granted
Links
- 238000005259 measurement Methods 0.000 title claims abstract description 20
- 238000002485 combustion reaction Methods 0.000 claims abstract description 25
- 230000002123 temporal effect Effects 0.000 claims abstract description 24
- 238000004364 calculation method Methods 0.000 claims description 19
- 238000000034 method Methods 0.000 claims description 8
- 238000012360 testing method Methods 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 4
- 238000009530 blood pressure measurement Methods 0.000 abstract description 2
- 239000007789 gas Substances 0.000 description 23
- 239000000446 fuel Substances 0.000 description 21
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 15
- 238000002347 injection Methods 0.000 description 8
- 239000007924 injection Substances 0.000 description 8
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 6
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 4
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 4
- 239000003546 flue gas Substances 0.000 description 4
- 230000006835 compression Effects 0.000 description 3
- 238000007906 compression Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 239000007788 liquid Substances 0.000 description 3
- 229910052757 nitrogen Inorganic materials 0.000 description 3
- 239000001569 carbon dioxide Substances 0.000 description 2
- 229910002092 carbon dioxide Inorganic materials 0.000 description 2
- 229910002091 carbon monoxide Inorganic materials 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000011144 upstream manufacturing Methods 0.000 description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000003085 diluting agent Substances 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 230000020169 heat generation Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- VUZPPFZMUPKLLV-UHFFFAOYSA-N methane;hydrate Chemical compound C.O VUZPPFZMUPKLLV-UHFFFAOYSA-N 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- 229910001868 water Inorganic materials 0.000 description 1
Classifications
-
- 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 aim of the invention is therefore to provide a system for controlling the state parameters of an engine that makes 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 avoid 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 cylinder internal pressure and from a measurement of at least one engine state 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, of the neural network type, the statistical type or the type of kriging.
- the measurement of the internal pressure of the cylinder can be carried out by means of a pressure sensor. It should be noted that each cylinder 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 carrying out preliminary tests.
- a method for estimating at least one state parameter of an internal combustion engine comprising at least one cylinder and a movable piston driven through a crankshaft, comprises the following steps : A first step of measuring the temporal variation of the crankshaft angle and the internal cylinder pressure; a second step of calculating, through at least one physical model, a plurality of intermediate time variables from said crankshaft angle and pressure measurements; internal cylinder and from a measurement of at least one engine state parameter; a third step of discretizing said intermediate temporal variables, intended for creating tables of discrete temporal variables; and a fourth step of estimating, through a learning model, at least one engine state parameter from said discrete time variable tables.
- the internal combustion engine 2 comprises a cylinder 3 in which a piston 4 moves by means of an internal combustion engine 2. 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 optionally mixed with a part of the exhaust gas, and the exhaust gas manifold 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 moment the angle ⁇ of the crankshaft 6, the sensor 21 makes it possible to measure the internal pressure P cy ⁇ 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 ⁇ ,
- the intermediate time variables may be, for example, cylinder temperature T cy u heat release Q, the mass fraction of gas burned X b , the mass of liquid fuel M car bu q and vaporized M car b vap , the mass of fresh gas M gf and flue gas M g b, the rate of burned gas X g b, 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 ⁇ ⁇ nj or the fuel mass introduced for each injection M ⁇ nj . 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 moments 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 cy ⁇ of the cylinder 3 thus makes it possible to construct intermediate temporal variables in order to deduce from it certain parameters of the state of 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 cy u ⁇ 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 7Vw.
- the cylinder temperature can be calculated thanks to the ideal gas law:
- the total mass M t can be determined by mapping according to the engine speed and the pressure of the intake manifold 9a.
- V cy ⁇ The volume of the cylinder V cy ⁇ is determined by measuring the angle ⁇ of the crankshaft 6.
- An analytical law makes it possible to determine V cy ⁇ as a function of ⁇ :
- K yl [ ⁇ ] V 1n + S p ⁇ • R n + L b ⁇ - R n • cos (0) - 4L b ? - Rj - ⁇ n ⁇ ) 2
- V 1n is the dead volume
- S p ⁇ is the surface of the piston
- R v ⁇ is the radius of the crankshaft
- Lbi is the connecting rod length
- Heat release Q represents heat exchanges between the gas and the outside, during chemical reactions that take place during the combustion phase of the fuel. That is, it represents the sum of the heat released by combustion minus the heat lost at the walls. Q is calculated as follows:
- ⁇ is the angle of the crankshaft ⁇ is the ratio of the specific heats C P IC V where C p and C v are the mass heats, respectively at constant pressure and volume
- Model 233 calculation of the mass fraction of burnt gases Xt 1 .
- the mass fraction of burnt gases X b evolves during combustion.
- An image of X b can be obtained by the release of heat Q.
- the heat released is proportional to the mass of fuel burned.
- the integral of heat Q is directly related to the mass of fuel already burnt. This integral is normalized between 0 and 1. It represents then the evolution of the combustion. It is called X b .
- ⁇ is the angular velocity of the motor in radians per second.
- variable Xb thus calculated is transmitted directly to the discretization module 24.
- Model 234 calculation of the mass of liquid fuel M r .arh_n q and vaporized M rnr h van.
- the state parameters of the motor transmitted by the connection 22a it is possible to use the phasings of each fuel injection ⁇ in] , the mass of fuel introduced for each injection M nj , as well as the duration of each injection T nj . Thanks to these parameters, it is possible to reconstruct the injection rate in the cylinder 3. This makes it possible to calculate:
- ⁇ nj Qm is the average fuel flow rate injected qm vap is the vaporized fuel flow rate Qm means com b is the fuel flow combustion means also:
- PCI is the lower heating value (about 43500 kJ / lcg for diesel).
- 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 ⁇ and of burnt gas Mg J1 .
- 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.
- initial mass of fresh gas M gf _ t mixture of oxygen and nitrogen
- the evolution of the fresh gases towards the flue gases is dependent on the heat generation Q. In fact the combustion always takes place locally with the richness 1, that is to say that when one burns 1 gram of fuel one burns on average 14.7 grams of fresh gas.
- Qwig f .gb is the average flow of air Which allows to calculate:
- the variables M g / and M g b thus calculated are transmitted as inputs to the physical model 236, as well as to the discretization module 24.
- Model 236 Calculation of the burnt gas ratio X ⁇ , diluent used to reduce NO 21 emissions.
- This burnt gas content X g t 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.
- Model 237 calculation of the polytropic coefficient in compression phase k.
- ⁇ is the angle of the current crankshaft -
- a ⁇ is the calculation interval
- This calculation interval A ⁇ may correspond to at least one sampling step of the internal pressure signal of the cylinder P cy ⁇ as a function of the angle ⁇ of the crankshaft 6. In general, the interval is taken from the order of 10 sampling steps of said signal.
- the 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 statistical data processing.
- - ordinary kriging the stationary variable has an unknown mean
- - universal kriging the variable is non-stationary.
- 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 estimation of a variable can be written in the following form:
- X 1 represent the discretizations of the variables
- the X 1 are the tables of discretized variables obtained by the module 24, the y (x t ) are the values of the variables which one wishes to estimate, like for example the filling ⁇ v .
- the principle of kriging is to determine the X 1 coefficients, which are dependent on the X 1 , by studying the degree of similarity between the y (X 1 ) from the covariance between the points x, as a function of the distance between these points. .
- the weights X 1 associated with each of the values y (x ⁇ ) are chosen so as to obtain a prediction y of minimum variance.
- the learning models 28 are thus previously identified on tests carried out on the engine test bench or on the vehicle.
Landscapes
- 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)
Abstract
Description
Claims
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 |
Publications (2)
Publication Number | Publication Date |
---|---|
EP2195519A1 true EP2195519A1 (fr) | 2010-06-16 |
EP2195519B1 EP2195519B1 (fr) | 2012-10-03 |
Family
ID=39273259
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP08837795A Active EP2195519B1 (fr) | 2007-10-12 | 2008-08-19 | Estimation de parametres d'etat d'un moteur par mesure de la pression interne d'un cylindre |
Country Status (3)
Country | Link |
---|---|
EP (1) | EP2195519B1 (fr) |
FR (1) | FR2922262B1 (fr) |
WO (1) | WO2009047412A1 (fr) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105488246A (zh) * | 2015-11-13 | 2016-04-13 | 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 |
Families Citing this family (4)
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. |
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 |
BR102022009523A2 (pt) * | 2022-05-16 | 2023-11-21 | Robert Bosch Limitada | Método para rastreamento de emissões de gases de efeito estufa |
Family Cites Families (5)
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 |
-
2007
- 2007-10-12 FR FR0758268A patent/FR2922262B1/fr not_active Expired - Fee Related
-
2008
- 2008-08-19 EP EP08837795A patent/EP2195519B1/fr active Active
- 2008-08-19 WO PCT/FR2008/051510 patent/WO2009047412A1/fr active Application Filing
Non-Patent Citations (1)
Title |
---|
See references of WO2009047412A1 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105488246A (zh) * | 2015-11-13 | 2016-04-13 | 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 |
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 |
Also Published As
Publication number | Publication date |
---|---|
FR2922262A1 (fr) | 2009-04-17 |
FR2922262B1 (fr) | 2010-03-12 |
EP2195519B1 (fr) | 2012-10-03 |
WO2009047412A1 (fr) | 2009-04-16 |
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