WO1996005421A1 - Procede et systeme de commande de moteurs a combustion - Google Patents

Procede et systeme de commande de moteurs a combustion Download PDF

Info

Publication number
WO1996005421A1
WO1996005421A1 PCT/SE1995/000914 SE9500914W WO9605421A1 WO 1996005421 A1 WO1996005421 A1 WO 1996005421A1 SE 9500914 W SE9500914 W SE 9500914W WO 9605421 A1 WO9605421 A1 WO 9605421A1
Authority
WO
WIPO (PCT)
Prior art keywords
neural net
engine
output signal
primary value
nodes
Prior art date
Application number
PCT/SE1995/000914
Other languages
English (en)
Inventor
Jan Nytomt
Original Assignee
Mecel Ab
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 Mecel Ab filed Critical Mecel Ab
Publication of WO1996005421A1 publication Critical patent/WO1996005421A1/fr

Links

Classifications

    • 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
    • F02D41/26—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means using computer, e.g. microprocessor
    • F02D41/266—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means using computer, e.g. microprocessor the computer being backed-up or assisted by another circuit, e.g. analogue
    • 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
    • F02D41/1438—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor
    • F02D41/1473—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor characterised by the regulation method
    • 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
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02P—IGNITION, OTHER THAN COMPRESSION IGNITION, FOR INTERNAL-COMBUSTION ENGINES; TESTING OF IGNITION TIMING IN COMPRESSION-IGNITION ENGINES
    • F02P5/00—Advancing or retarding ignition; Control therefor
    • F02P5/04—Advancing or retarding ignition; Control therefor automatically, as a function of the working conditions of the engine or vehicle or of the atmospheric conditions
    • F02P5/145—Advancing or retarding ignition; Control therefor automatically, as a function of the working conditions of the engine or vehicle or of the atmospheric conditions using electrical means
    • F02P5/15—Digital data processing
    • F02P5/1502—Digital data processing using one central computing unit
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks 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
    • F02D41/02—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
    • F02D41/1438—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor
    • F02D41/1444—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor characterised by the characteristics of the combustion gases
    • F02D41/1454—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor characterised by the characteristics of the combustion gases the characteristics being an oxygen content or concentration or the air-fuel ratio
    • F02D41/1456—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor characterised by the characteristics of the combustion gases the characteristics being an oxygen content or concentration or the air-fuel ratio with sensor output signal being linear or quasi-linear with the concentration of oxygen
    • 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

  • This invention relates to a method specified in accordance with the preamble of claim 1, and a system specified in accordance with the preamble of claim 9.
  • engine parameters influencing the amount of fuel or ignition timing used, increasing or decreasing the amount of fuel or advancing or retarding the ignition timing.
  • engine parameters could be the engine temperature, which could constitute a third axis in a three- dimensional table, and corrective engine parameters such as derivative of throttle position , derivative of revs, inlet air pressure (often used in supercharged engines), air temperature, knock intensity, current lambda- value, or in some cases also the speed of the vehicle driven by the engine.
  • These corrective engine parameters could give correction values for the control parameters, which correction values are stored in tables or alternatively will be given by predetermined functions. The control will hence be rather complex and demanding an extended memory capacity for storing the often very extensive tables.
  • an artificial neural net In automotive use a new technique for controlling different type of systems has instead employed an artificial neural net.
  • DE.A.4211556 is an artificial neural net used for modelling the reaction of the driver and the behaviour of the vehicle in real environments, alerting the driver for hazards and supporting the guidance of the vehicle.
  • DE,A,4300844 is an artificial neural net used for adapting the characteristics of the vehicle to the driving behaviour of the driver.
  • Automotive Engineering, SAE congress issue, February 1993, page's 52-55 is shown an engine suspension system, having an artificial neural net being able to learn from the detected oscillations of the engine and the control actions taken, with a view to optimise the damping capacity.
  • the object of the invention is obtaining a smoother control of the combustion engine, and reducing the required capacity of memory for the control system of the combustion engine.
  • Another object is to simplify, with the right kind of equipment for authorised service-facilities and during manufacturing, re-programming of the control system of the combustion engine, in order to meet various demands put on emissions and requests concerning engine character and response. Entering the tables and modifying all of the control data contained for the different operating cases, which must be done in the state of the art type of systems, is a very time consuming process that has to be initiated when the conditions have changed.
  • Yet another object is to meet in an efficiently manner the stricter demands on low levels of emissions, demanding more accurate control of the combustion process and further input data from the engine i form of engine parameters detected.
  • Yet another object is to obtain more information for the control than could be obtained from the representation of each individual input data. Combinations of input data could also be used by the inventive control in order to obtain an optimal control.
  • the inventive method is characterised by the characterising clause of claim 1.
  • FIGURES Figure 1 shows a model of a neural net used by the inventive method, controlling a combustion engine
  • Figure 2 shows schematically the engine parameters required by the combustion engine control system (CPU) for controlling the control data, i.e. ignition timing or fuel amount
  • Figure 3 shows a control system for fuel supply, having a temporary connected control unit 10, for the learning process of the neural net.
  • CPU combustion engine control system
  • the inventive method for controlling the control data for a combustion engine use so-called artificial intelligence in form of a neural network.
  • a neural network implemented for controlling the control data of a combustion engine functions in such a way that for each operating case, i.e. given by input data in form of detected parameters of the engine, is the neural net learnt to generate an output signal or output data dependent of the input data received.
  • FIG 1 a neural net having a number of nodes(En-Ei5, E 2 ⁇ -E 23 , E 3 ⁇ -E3 2 , .. , E-, which are organised in a number n of layers. All nodes in the first layer are input data nodes, each node respectively connected to every node in the next layer being a so-called hidden layer.
  • the first layer having the input data nodes(E u -Ei 5 ), is followed by at least one bidden laye E ⁇ -E ⁇ ), and in some cases could several hidden layers follow as indicated by the nodes (E 3 ⁇ -E 32 ) having dotted contours in figure 1.
  • the neural net is terminated by one or several output data nodes E n!
  • the output signal from each node and layer is transmitted only to each node in next succeeding layer.
  • the input data signals are sent to the input data nodesCE ⁇ -Eis), in such a way that a unique value is received by each respective input data node.
  • For the control of the combustion engine could for example transformed or re-scaled values, representing the engine speed, load, engin temperature, throttle position and temperature of ambient air, be fed to its respective node En -E1 5 . Re-scaling is performed in order to improve the performance, such that each value could assume a value in the range between 0 to 1 , or any other suitable measuring range.
  • the output signal from the input data nodes constitutes nothing else than the re-scaled or transformed values for the corresponding magnitude of the input data.
  • the output signal from each remaining node constitutes the sum of the output signals from preceding nodes according a simple and preferably logical function having a weight factor W at each node.
  • the adaptation or learning process of the neural net is performed by using a non-linear approximation of a function, and preferably is a so-called Backpropagation method used, where the weight factor W of each individual node is adjusted in such a manner that th output signal of the neural net corresponds to the desired output signal.
  • the number of nodes for example input data nodes, and the number of hidden layers are adapted to each type of control method, in such a way that the least deviation between the desired output signal and the output signal from the neural net is minimised as fast as possible, with all available input data designated to an individual input data node.
  • the weight factor W could at the first start of the learning process be given an individual random value.
  • the neural net is to be updated for a slightly modified output, possibly motivated by changed emission demands or emission test cycles, could preferably the previously used factors of weight be used at start in order to shorten the learning process.
  • figure 2 is shown a survey of the types of input data required for controlling a combustion engine, such that an optimal combustion and reduced fuel consumption could be obtained
  • the momentary engine parameters which controls the amount of fuel or the ignition timing is primarily the speed E-pm and load Th(throttle position), but also the derivative of these parameters, d/dt E-p m and d/dt Th, the inlet air pressure P ⁇ , the temperature of the inlet air T m , and in some cases also the speed of the vehicle Speedv* driven by the combustion engine.
  • a calculation of the air mass supplied be used in order to establish the current load, either by using the parameters P.,-, and T m , or alternatively using hot-wire detectors arranged in the inlet manifold. These latter methods are often used in systems needing a more accurate control of the lambda value.
  • An additional number of engine parameters are detected in order to obtain a feed back from the combustion, such that the amount of fuel or the ignition timing could be adjusted to a more optimal amount of fuel or ignition timing.
  • engine feed back parameters could be a knock signal, Knock, from a special knock detecting circuit, a signal from a lambda sensor, ⁇ , indicating the residual amount of air in the exhaust gases, or a signal Ion curre -. t obtained from a circuit detecting the degree of ionisation within the combustion chamber.
  • a knocking condition is damaging to the engine and results in a non optimal usage of the fuel.
  • the ignition timing In order to terminate the knocking condition is the ignition timing usually retarded or alternatively complemented by increasing the fuel amount supplied.
  • the signal from the lambda sensor is used in order to maintain a desired air-fuel mixture, specifically in Otto engines having a three-way catalyst with optimal function at a lambda value of 1.0.
  • Another feed back signal from the combustion is the ionisation current in a measuring gap arranged in the combustion chamber.
  • the ionisation current could in a simple manner detect a misfire, causing ionisation failure.
  • FIG 3 is schematically shown a control system for fuel supply to a combustion engine 1, having a neural net control implemented in a microcomputer based control unit 11 , and a temporary connected learning computer 10 used during the learning process controlling the fuel amount supplied and optimising the output signal of the neural net.
  • the control system consists of two units during the learning process, a permanent control unit 15 and a temporary unit 13, which temporary unit only is connected during the learning process and disconnected at the interface 14, indicated with dots.
  • the interface 14 having conventional common connector means connecting all signal lines.
  • An additional lambda sensor 5 is also arranged in the exhaust system 4 for the learning process.
  • the lambda sensor 5 being a linear type of lambda sensor, which unlike a narrow banded conventional lambda sensor have an output signal being proportional to the residual amount of air in the exhaust gases.
  • the conventional type of narrow banded lambda sensor have a distinctive transition and hence only able to detect if the lambda value is below or above 1.0.
  • the linear type of lambda sensor is required when performing a more accurate control, which is a prerequisite for obtaining a proper result from the learning process.
  • the linear type of lambda sensor is more expensive than a conventional narrow banded lambda sensor, which is the major reason why mass produced engines are equipped with narrow banded lambda sensors.
  • Oine 23 is divided into separate lines for each injector).
  • the learning process of the neural net is the amount of fuel supplied controlled by the learning computer 10 through control pulses at the output terminal 21, which output terminal is connected to each individual line 23 via a switching circuit 12.
  • the learning computer 10 is activated or connected, is the switching circuit automatically forced to switch to terminal 12b, from a preferably stable terminal position 12a, and the output signal at output terminal 22 of the neural net controlled computer 11 could not be transmitted to the fuel injector, respectively.
  • the present engine parameters are detected by sensors 6 arranged at the engine, only one sensor shown in the figure, and these inp signals are transmitted to learning computer as well as the neural net controlled computer 11.
  • the neural net will thus produce an output signal dependent of the present input signals.
  • the learning computer will simultaneously calculate the necessary amount of fuel on a basis of the detected input signals, and will supply an output signal to each individual fuel injector correspondin to the necessary amount of fuel .
  • the output signal from the output terminal 20 of the learning computer 10 is also connected to an input terminal of the neural net controlled computer, which output signal is used as target value for the adjustment of the output signal from the neural net.
  • the learning computer detects the proper lambda value, i.e.
  • the learning computer 11 send a signal 24 to the neural net controlled computer 11.
  • this signal is the proper operating case assumed, in thi case an operating case optimised regarding emissions, and the present output signal from the neural net is compared with the output signal at output terminal 21 of the learning computer.
  • the content of the neural net i.e. the established weighted function for each node respectively, is thereafter optimised in such a manner that the resulting output signal from the neural net approache the target value.
  • this optimisation/training of the neural net are the input signals supplied to the first layer of the neural net, followed by a successive propagation of signals through each layer the neural net until an output signal is obtained at the output node of the neural net.
  • the output sign from the neural net is thereafter compared with the output signal at output terminal 21 , and the weight factor of each node of the neural net is corrected in order to reduce the deviation between the output signal from the neural net and the output signal at output terminal 21.
  • This correction or learning procedure for the weight factors could preferably be performed by using a so-called “Backpropagation-method", see textbook of Mr Kosko, which "Backpropagation” is performed a repeatedly number of times until the deviation is at an acceptable level. In some cases it could be necessary to repeat this procedure between 10 6 -10 7 times during the learning process, before the output signal from the neural net in an acceptable manner corresponds to the desired target value.
  • a predetermined acceptable level of deviation between the output signal from the neural net and the target value could be one or some percent, which should be obtainable if the right choice of neural net model is used and an adequate number of corrections of the weight factors is performed, for example by using "Backpropagation". If insufficient correspondence is obtained, then the neural net model must be revised.
  • the ignition timing instead of fuel amount, could in the same manner as described with reference to figure 3, be controlled by a neural net controlled computer.
  • the output signal from the neural net is then during the learning process compared with a signal representative for an ignition timing target value.
  • the ignition timing target value is then preferably obtained from the learning computer 10, and in the corresponding manner is the deviation between the representative signal of the ignition timing target value and the output signal from the neural net minimised.
  • a supercharged combustion engine could also the charge pressure be controlled in a corresponding manner by a neural net controlled microcomputer.
  • the charge pressure level is most often given from value stored in a table dependent of the present load and speed.
  • a learning system corresponding to that shown in figure 3 be used.
  • the line 23 will then instead control preferably a so-called waste-gate, or any similar control device for the supercharging unit.
  • the system shown in figure 3 could also include a narrow banded lambda sensor among the sensors being permanently attached to the engine, which sensors are only schematically represented as one common symbol in the drawing.
  • the linear type of lambda sensor 5 will then constitute a second lambda sensor that only will be used during the learning process of the neural net.
  • the conventional narrow banded lambda sensor be excluded, but the trained neural net will automatically strive in its mode of control maintaining a lambda 1.0 value, this being the base for the generation of control data in question (amount of fuel or ignition timing) from the neural net.
  • the fewest amounts of input parameters supplied to the first layer of the neural net be no more than two, i.e. speed and load.
  • derivative of throttle position and derivative of speed be supplied as input signals to individual input data nodes of the first layer of the neural net.
  • the neural net controlled microcomputer 11 will then include means for detecting speed as well as calculation of the derivative of speed, as deduced for example from a crankshaft sensor.
  • the throttle position be obtained from a throttle position sensor, or if an electrically controlled throttle is used (the position of the throttle being controlled by an electric motor independently of the throttle pedal position) could the control signal sent to the throttle motor constitute the throttle signal, and the calculation of throttle derivative could be performed by the neural net controlled microcomputer.
  • All signals are transformed or re-scaled such that the input data signals supplied to the first layer of the neural net have similar signal range, preferably between 0-1 volts, which facilitate the learning process of the neural net.
  • the load signal could in very simple system only be constituted by the throttle position, especially in aspirating engines, but for example in supercharged combustion engines is the load calculated from the detected amount of air supplied to the engine, which amount of air could be calculated from the detected pressure and temperature of the air supplied to the engine. This amount of air could preferably be calculated by the neural net controlled microcomputer 11 , and a signal representative for the amount of air is supplied to an input data node in the first layer of the neural net.
  • the neural net thus trained will for each identical combination of input signals repeatedly issue a corresponding primary value of the control data in concern, in a corresponding manner as a primary value will be given the control data from a table or map.
  • a possibly detected knocking condition or the output signal from a narrow banded lambda sensor arranged in the exhaust system could in a known manner cause a correction of the primary value of the control data given by the neural net, according an established corrective routine.
  • a knocking condition often treated as binary condition (on off) is often controlled such that the control data is corrected in one larger corrective step at the instant of knock detection, followed by return to the ideal primary value given by the neural net or the table/map in successive incremental steps of a smaller order.
  • Knock control could also be performed according more sophisticated routines, with a view to terminating the knocking condition more quickly at minimum deviation from the ideal condition.
  • An initially detected knocking condition occurring during ideal conditions i.e. when the control data is equivalent to the control data given by the neural net, could cause a corrective step having another order of size than a corrective step initiated during a recurring knocking condition which occurs when the control data is already subjected to corrective actions.
  • Such control routines of higher intelligence could preferably be located externally of or after the neural net, i.e. the knock related signal is not supplied as input data to the neural net, and the corrective action taken of the primary value of the control data given by the neural net is performed as an additional routine.
  • the knocking detection circuit is supplying and adapted input signal to a input data node in the first layer of the neural net, where the input data signal is dependent of whether or not a knocking condition have been detected or if the knocking condition is a so-called recurring knocking condition occurring during initiated corrective actions of the control data, and where the input signal supplied to the input data node will be returned to a value representative for a non- knocking condition according a predetermined function given by the knocking detection circuit.
  • each individual neural net could as an input data signal use an output signal from one or more neural nets.
  • the signal representative for the fuel target value i.e. the amount of fuel requested from a learning computer
  • the signal representative for the ignition timing be supplied as an input data signal to the neural net for the fuel amount control.
  • Each neural net could hence be adapted to its own function, and the complexity of the neural net, i.e. the number of layers and nodes, could be reduced.
  • the learning process could be performed in a laboratory as well as in an operating vehicle. It is also possible to repeat the learning process as a service action, when the combustion engine has been operating for a certain period causing wear and other changes in the operating conditions that might require modification. This is advantageously due to that real operating conditions could be tested and adjustments made in the real environment. Another advantage is that for each learning process could the operating cases of most interest be chosen, and the system could be trained more vigorously for these special operating cases.
  • the neural net control lead to that it will become more difficult doing unauthorised changes of the control function, while at the same time admitting during manufacturing optimisation of the control function according different conditions such as emission, response and/or fuel consumption, dependent of how and where the combustion engine should be used
  • Unauthorised tuning is done frequently in microcomputer controlled systems, where the table containing the control data is stored and easily could be read and modified and stored in a new EPROM-memory, due to that the table is stored in a restricted area of the memory and in a relatively easy manner could be interpreted.
  • the weight factors of the neural net could not as easily be modified without having in depth knowledge of neural nets, and a modification of any or some weight factors could obtain limited improvements of special purposes for some specific operating cases , but where other operating cases obtain changes for the worse.
  • the need for usage of E 2 PROM or FLASH- type of memories, which are more difficult to copy and change as well as more expensive, is therefore reduced considerably.

Landscapes

  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Computer Hardware Design (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Theoretical Computer Science (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Microelectronics & Electronic Packaging (AREA)
  • Electrical Control Of Ignition Timing (AREA)
  • Combined Controls Of Internal Combustion Engines (AREA)

Abstract

Procédé de commande d'un moteur à combustion utilisant un réseau neuronal commandant les valeurs primaires, de préférence le réglage d'allumage ou la quantité de carburant, mais également la pression de charge, et un système d'apprentissage pour le réseau neuronal. L'utilisation au moins du régime et de la charge du moteur comme signaux d'entrée appliqués à un réseau neuronal, et l'apprentissage par celui-ci de la manière de déterminer la valeur nécessaire de la valeur primaire en fonction des paramètres d'entrée, permettent d'obtenir une commande plus douce du moteur à combustion, sans transitions brusques entre différentes conditions de fonctionnement. Cette commande fondée sur un réseau neuronal présente des avantages considérables par rapport à la méthode classique, selon laquelle des valeurs relatives aux quantités de carburant ou au réglage d'allumage sont stockées dans une table en contenant les valeurs primaires. L'apprentissage du réseau neuronal se fait à l'aide d'un ordinateur d'apprentissage (10) relié temporairement, possédant une sonde lambda linéaire supplémentaire (5), et la valeur courante de la valeur primaire calculée par l'ordinateur d'apprentissage est utilisée comme valeur cible lors de l'optimisation des facteurs de pondération du réseau neuronal, cette optimisation étant exécutée afin de réduire l'écart entre le signal de sortie du réseau neuronal et le signal de sortie de l'ordinateur d'apprentissage. Lorsque ce dernier est déconnecté, au moment où le processus d'apprentissage du réseau neuronal est achevé et l'écart se situe dans des limites acceptables, un commutateur (12) relie le réseau neuronal afin de permettre la commande ultérieure de la valeur primaire.
PCT/SE1995/000914 1994-08-11 1995-08-08 Procede et systeme de commande de moteurs a combustion WO1996005421A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
SE9402687-9 1994-08-11
SE9402687A SE509805C2 (sv) 1994-08-11 1994-08-11 Metod och system för reglering av förbränningsmotorer

Publications (1)

Publication Number Publication Date
WO1996005421A1 true WO1996005421A1 (fr) 1996-02-22

Family

ID=20394895

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/SE1995/000914 WO1996005421A1 (fr) 1994-08-11 1995-08-08 Procede et systeme de commande de moteurs a combustion

Country Status (2)

Country Link
SE (1) SE509805C2 (fr)
WO (1) WO1996005421A1 (fr)

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2315133A (en) * 1996-07-08 1998-01-21 Richard Nigel Bushell Control system for internal combustion engine
WO1998037321A1 (fr) * 1997-02-20 1998-08-27 Schroeder Dierk Procede et dispositif de regulation du melange dans un moteur a combustion interne
FR2772427A1 (fr) * 1997-12-12 1999-06-18 Renault Systeme de controle du moteur d'un vehicule par reseaux de neurones
US6289275B1 (en) * 1995-02-13 2001-09-11 Chrysler Corporation Neural network based transient fuel control method
DE102004031006A1 (de) * 2003-07-23 2005-04-28 Daimler Chrysler Ag Verbrennungsmotor und Verfahren zur Verbrennungskenngrößenbestimmung
FR2864155A1 (fr) * 2003-12-19 2005-06-24 Renault Sas Procede et systeme d'estimation de la temperature de 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
FR2987403A1 (fr) * 2012-02-24 2013-08-30 Peugeot Citroen Automobiles Sa Dispositif de commande d'allumage anti-cliquetis de moteur a combustion de vehicule automobile
EP2793141A4 (fr) * 2011-12-12 2015-10-28 Toyota Motor Co Ltd Dispositif de commande de moteur
US20190325671A1 (en) * 2018-04-20 2019-10-24 Toyota Jidosha Kabushiki Kaisha Machine learning device of amount of unburned fuel, machine learning method, learned model, electronic control unit, method of production of electronic control unit, and machine learning system
US20200088120A1 (en) * 2018-09-14 2020-03-19 Toyota Jidosha Kabushiki Kaisha Control device of internal combustion engine
WO2023242088A1 (fr) * 2022-06-17 2023-12-21 Vitesco Technologies GmbH Système et procédé de détermination d'une grandeur dans un véhicule automobile

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6741087B1 (ja) * 2019-02-01 2020-08-19 トヨタ自動車株式会社 内燃機関の制御装置、車載電子制御ユニット、機械学習システム、内燃機関の制御方法、電子制御ユニットの製造方法及び出力パラメータ算出装置

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0441522A2 (fr) * 1990-02-09 1991-08-14 Hitachi, Ltd. Appareil de commande pour automobile
US5093792A (en) * 1988-05-31 1992-03-03 Kabushiki Kaisha Toyota Chuo Kenkyusho Combustion prediction and discrimination apparatus for an internal combustion engine and control apparatus therefor
US5247445A (en) * 1989-09-06 1993-09-21 Honda Giken Kogyo Kabushiki Kaisha Control unit of an internal combustion engine control unit utilizing a neural network to reduce deviations between exhaust gas constituents and predetermined values

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5093792A (en) * 1988-05-31 1992-03-03 Kabushiki Kaisha Toyota Chuo Kenkyusho Combustion prediction and discrimination apparatus for an internal combustion engine and control apparatus therefor
US5247445A (en) * 1989-09-06 1993-09-21 Honda Giken Kogyo Kabushiki Kaisha Control unit of an internal combustion engine control unit utilizing a neural network to reduce deviations between exhaust gas constituents and predetermined values
EP0441522A2 (fr) * 1990-02-09 1991-08-14 Hitachi, Ltd. Appareil de commande pour automobile

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6289275B1 (en) * 1995-02-13 2001-09-11 Chrysler Corporation Neural network based transient fuel control method
GB2315133A (en) * 1996-07-08 1998-01-21 Richard Nigel Bushell Control system for internal combustion engine
WO1998037321A1 (fr) * 1997-02-20 1998-08-27 Schroeder Dierk Procede et dispositif de regulation du melange dans un moteur a combustion interne
FR2772427A1 (fr) * 1997-12-12 1999-06-18 Renault Systeme de controle du moteur d'un vehicule par reseaux de neurones
DE102004031006A1 (de) * 2003-07-23 2005-04-28 Daimler Chrysler Ag Verbrennungsmotor und Verfahren zur Verbrennungskenngrößenbestimmung
FR2864155A1 (fr) * 2003-12-19 2005-06-24 Renault Sas Procede et systeme d'estimation de la temperature de gaz d'echappement et moteur a combustion interne equipe d'un tel systeme
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
EP2793141A4 (fr) * 2011-12-12 2015-10-28 Toyota Motor Co Ltd Dispositif de commande de moteur
FR2987403A1 (fr) * 2012-02-24 2013-08-30 Peugeot Citroen Automobiles Sa Dispositif de commande d'allumage anti-cliquetis de moteur a combustion de vehicule automobile
US20190325671A1 (en) * 2018-04-20 2019-10-24 Toyota Jidosha Kabushiki Kaisha Machine learning device of amount of unburned fuel, machine learning method, learned model, electronic control unit, method of production of electronic control unit, and machine learning system
US10991174B2 (en) * 2018-04-20 2021-04-27 Toyota Jidosha Kabushiki Kaisha Machine learning device of amount of unburned fuel, machine learning method, learned model, electronic control unit, method of production of electronic control unit, and machine learning system
US20200088120A1 (en) * 2018-09-14 2020-03-19 Toyota Jidosha Kabushiki Kaisha Control device of internal combustion engine
US11047325B2 (en) * 2018-09-14 2021-06-29 Toyota Jidosha Kabushiki Kaisha Control device of internal combustion engine
WO2023242088A1 (fr) * 2022-06-17 2023-12-21 Vitesco Technologies GmbH Système et procédé de détermination d'une grandeur dans un véhicule automobile
FR3136864A1 (fr) * 2022-06-17 2023-12-22 Vitesco Technologies Système et procédé de détermination d’une grandeur dans un système de motorisation de véhicule

Also Published As

Publication number Publication date
SE509805C2 (sv) 1999-03-08
SE9402687L (sv) 1996-02-12
SE9402687D0 (sv) 1994-08-11

Similar Documents

Publication Publication Date Title
US4080947A (en) Apparatus and method for controlling ignition of multi-cylinder internal combustion engines with a passageway that bypasses throttle valve
US4126107A (en) Electronic fuel injection system
US4064844A (en) Apparatus and method for successively inactivating the cylinders of an electronically fuel-injected internal combustion engine in response to sensed engine load
US4244023A (en) Microprocessor-based engine control system with acceleration enrichment control
WO1996005421A1 (fr) Procede et systeme de commande de moteurs a combustion
EP0589517A1 (fr) Procédé pour la prédiction de l'écoulement d'air dans un cylindre
US4377996A (en) Ignition timing control method and system
US4736724A (en) Adaptive lean limit air fuel control using combustion pressure sensor feedback
US5611754A (en) Control system for internal combustion engines
US4933862A (en) Engine control apparatus
US4715343A (en) Method and apparatus for controlling heater for heating air-fuel ratio sensor
US5058550A (en) Method for determining the control values of a multicylinder internal combustion engine and apparatus therefor
US6668795B1 (en) Controller with feed-back system
CA1131737A (fr) Dispositif de commande-regulation pour moteur a combustion interne
CN101566107A (zh) 通用型跟踪空气燃料比调节器的离线标定
CA1189591A (fr) Compensateur d'atomisation pour systeme d'injection electronique de carburant
US5016181A (en) Method and system for an engine ignition timing control
US4732128A (en) Method and apparatus for controlling heater for heatng air-fuel ratio sensor
CN113266481A (zh) 车辆控制方法、车辆用控制装置以及服务器
EP0210766B1 (fr) Système de commande adaptive pour moteur à combustion interne
US4862855A (en) Control apparatus for internal combustion engine
US4548178A (en) Method and apparatus for controlling the air-fuel ratio in an internal-combustion engine
GB2366004A (en) A method for diagnosing cylinder bank dependant or cylinder bank independent faults in an internal combustion engine
US4561056A (en) Electronic control apparatus for internal combustion engine
JP2004100495A (ja) 内燃機関の制御装置

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): DE US

REG Reference to national code

Ref country code: DE

Ref legal event code: 8642