EP3698036A1 - Calcul d'émissions de gaz d'échappement d'un véhicule à moteur - Google Patents

Calcul d'émissions de gaz d'échappement d'un véhicule à moteur

Info

Publication number
EP3698036A1
EP3698036A1 EP18793567.1A EP18793567A EP3698036A1 EP 3698036 A1 EP3698036 A1 EP 3698036A1 EP 18793567 A EP18793567 A EP 18793567A EP 3698036 A1 EP3698036 A1 EP 3698036A1
Authority
EP
European Patent Office
Prior art keywords
variables
machine learning
learning system
emissions
motor vehicle
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.)
Pending
Application number
EP18793567.1A
Other languages
German (de)
English (en)
Inventor
Heiner Markert
Martin Schiegg
Stefan ANGERMAIER
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.)
Robert Bosch GmbH
Original Assignee
Robert Bosch GmbH
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 Robert Bosch GmbH filed Critical Robert Bosch GmbH
Publication of EP3698036A1 publication Critical patent/EP3698036A1/fr
Pending 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
    • F02D41/26—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means using computer, e.g. microprocessor
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
    • F01N11/00—Monitoring or diagnostic devices for exhaust-gas treatment apparatus, e.g. for catalytic activity
    • F01N11/007—Monitoring or diagnostic devices for exhaust-gas treatment apparatus, e.g. for catalytic activity the diagnostic devices measuring oxygen or air concentration downstream of the exhaust apparatus
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    • F02B—INTERNAL-COMBUSTION PISTON ENGINES; COMBUSTION ENGINES IN GENERAL
    • F02B77/00—Component parts, details or accessories, not otherwise provided for
    • F02B77/08—Safety, indicating, or supervising devices
    • F02B77/085—Safety, indicating, or supervising devices with sensors measuring combustion processes, e.g. knocking, pressure, ionization, combustion flame
    • F02B77/086—Sensor arrangements in the exhaust, e.g. for temperature, misfire, air/fuel ratio, oxygen sensors
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    • F02D41/1465—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 NOx content or concentration of the exhaust gases downstream of exhaust gas treatment apparatus with determination means using an estimation
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    • 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
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M15/00—Testing of engines
    • G01M15/04—Testing internal-combustion engines
    • G01M15/05—Testing internal-combustion engines by combined monitoring of two or more different engine parameters
    • G—PHYSICS
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    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M15/00—Testing of engines
    • G01M15/04—Testing internal-combustion engines
    • G01M15/10—Testing internal-combustion engines by monitoring exhaust gases or combustion flame
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    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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    • F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
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    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
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Definitions

  • the invention relates to a method and an apparatus for determining emissions, a computer program, and a machine-readable storage medium.
  • independent claim 1 has the advantage that particularly fast and easy emissions of a driven by an internal combustion engine motor vehicle in a practical driving operation can be determined.
  • Advantageous developments are the subject of the independent claims.
  • the legislation provides for the approval of new motor vehicles powered by an internal combustion engine depending on to make the emissions that result in practical driving.
  • new motor vehicles powered by an internal combustion engine depending on to make the emissions that result in practical driving.
  • the English term real driving emissions is also common.
  • motor vehicles include, for example, those that are driven exclusively by an internal combustion engine, but also those with a hybridized powertrain.
  • an inspector with the motor vehicle denies a driving cycle or several driving cycles and the resulting emissions are measured. The approval of the motor vehicle is then dependent on these measured emissions.
  • the driving cycle can be freely selected within wide limits by the examiner. A typical duration of a drive cycle may be 90-120 min, for example.
  • the invention therefore relates to a method for determining emissions of a motor vehicle driven by an internal combustion engine in a practical driving operation, wherein a machine learning system is trained thereon by means of measured time profiles (x) of operating variables of the internal combustion engine and / or of the motor vehicle Gradients ( ⁇ ') to generate operating variables of the internal combustion engine and / or the motor vehicle, and then to determine the emissions depending on these generated time courses ( ⁇ ').
  • the operational quantities may preferably comprise one, some or all of the sizes characterizing the following size:
  • time profiles of the operating variables it is also possible, depending on parameters which characterize the internal combustion engine and / or the motor vehicle in the cycle to be completed, to train time profiles ( ⁇ ') of operating variables.
  • a characterization of the driven route for example GPS records, ambient temperature, ambient pressure, etc.
  • the injections may in particular be multiple injections, for example a main injection, pre-injection and post-injection.
  • those of the motor vehicle and / or the internal combustion engine preferably include quantities which characterize each fuel quantity and injection time of each of the partial injections.
  • the machine learning system comprises a first part - an encoder - which first transforms the measured time courses (x) into first variables (z, qe (z
  • machine learning system comprises a second part - a decoder - which generates second quantities ( ⁇ ', ⁇ ( ⁇ '
  • the machine learning system is trained to extract the essential features, the realistic temporal courses - ie especially those courses which are contained in the training data of the measured time courses.
  • the nature of realistic trajectories is therefore coded in parameters that characterize the coder or the decoder.
  • the coder transforms the measured time courses (x) not only into latent variables (z), but also into additional variables depending on the characterizing parameters.
  • the first variables which characterize the latent variables (z) are the latent variables (z) themselves and wherein the second part is one of third parameters ( ⁇ ) parameterized Gaussian process model, and the third parameters ( ⁇ ) and the latent variables (z) are adapted during training of the machine learning system such that a marginal probability (p (x
  • the use of the Gaussian process model has the advantage that a probabilistic statement about whether a respective second variable (z) corresponds to a course having the same characteristics as the measured courses (x) or not is possible. This makes it possible to judge for any test cycles based on the corresponding associated courses of operating variables of the motor vehicle and / or the internal combustion engine, whether these are relevant or not in view of the measured courses that have entered the training phase.
  • the first part may comprise a function parametrized by the fourth parameter (v), such as a neural network, for example, and adaptation of the latent variables (z) during training by adaptation of the fourth parameter (v).
  • v a function parametrized by the fourth parameter (v)
  • An auto-encoder means that the first quantities that characterize the latent variables (z) are the latent variables themselves, and the second quantities that characterize the generated timings are the generated timings themselves.
  • the first part and the second part may be given, for example, by neural networks.
  • This method has the advantage that the training of the machine learning system is particularly simple.
  • parameters which parameterize the auto-decoder can be adapted in such a way that a cost function which includes a reconstruction error, for example a standard
  • the first part and the second part of the machine learning system form a two-part auto-encoder.
  • Variation Autoencoder are known in German under the English-language term ⁇ l anational autencoder.
  • first quantities which respectively characterize the latent variables (z) are a first probability distribution (q e (z
  • the training of the machine learning system then means that the first parameters ( ⁇ ) and the second parameters ( ⁇ ) are varied in such a way that a cost function is minimized.
  • the cost function advantageously comprises a reconstruction error of a generated reconstruction of the measured time courses and a Kullback-Leibler divergence KL [q (z)
  • a parameterizable distribution function for example a normal distribution
  • the first part and / or the second part can then each comprise, for example, a neural network which, depending on the input variables supplied to it, determines parameters which parameterize this parameterizable distribution function.
  • the advantage of using the multi-part autocoder is that the first probability distribution (q e (z
  • the first variables which characterize the latent variables (z) are the latent variables (z) themselves and the first part from measured temporal progressions (x) by means of a method of sparse dictionary learning
  • the sparse variables (z) determine which coefficients of the respective measured temporal courses (x) in the representation represent a linear combination of the dictionary learned by means of this method. In this way, the space of the latent variables (z) can be reduced particularly effectively.
  • latent variables (z) are specified and the machine learning system generates time profiles ( ⁇ ') of operating variables of the motor vehicle and / or the internal combustion engine as a function of these predetermined latent variables (z), and then the emissions are determined depending on these generated time courses.
  • the machine learning system is initially trained by means of measured time courses to be able to generate realistic temporal courses.
  • temporal courses are then generated, to which then, e.g. be determined with a suitable mathematical model such as a machine learning method or a physico-chemical model, emissions. It may also be provided to measure the emissions on a real system in order to train, for example, said machine learning method.
  • the at least some, preferably all, of the latent variables (z) will be determined by means of a method of statistical experimental design. This is particularly good if, depending on the emissions determined, the mathematical model used to determine the emissions should be adapted to actual measured emission values. This makes it possible to ensure that the widest possible areas of the latent variable space are explored as efficiently as possible.
  • the determined emissions are particularly representative of the emissions occurring in the real operation of the motor vehicle.
  • the emissions occurring in real operation of the motor vehicle can be estimated particularly accurately.
  • time gradients ( ⁇ ') are to be generated, which should be based on at least one predefinable property.
  • temporal profiles ( ⁇ ') can thus be generated, which are generated in a limited manner to the above-mentioned characterizing parameters, that is, for example, vehicle types or geographical locations, by specifying these characterizing parameters as additional variables.
  • the additional parameters which are to code the predefinable properties of the training course ( ⁇ ') generated during training are set to the true value during training (since this characteristic of the time course is known at training times) ,
  • the machine learning system comprises a first part - a discriminator - which either measured time courses of operating variables of the motor vehicle and / or the internal combustion engine or from a second part of the machine learning system - a generator - generated time profiles of the motor vehicle and / or the internal combustion engine, are fed
  • the first part is trained to be able to decide as well as possible whether a measured or a generated time course of operating variables of the motor vehicle and / or the internal combustion engine is supplied, wherein the second part is trained thereon, depending on randomly selected input variables temporal courses of operating variables of the motor vehicle and / or the internal combustion engine as possible to generate such that the first part can decide as bad as possible, whether a measured or a generated time course of operating variables of the motor vehicle and / or the internal combustion engine is supplied.
  • This method has the advantage that the thus generated courses of operating variables of the motor vehicle and / or the internal combustion engine are particularly realistic.
  • the meaning of the word "random” may include that the variables thus selected are determined by means of a true random number generator or by means of a pseudo-random number generator.
  • the training of the first part and the second part can advantageously be carried out alternately to ensure that the training is as effective as possible.
  • the training can expediently be continued until the discriminator is no longer able to differentiate with predeterminable accuracy, whether the temporal courses supplied to him are measured or generated by the generator over time.
  • the machine learning system generates temporal profiles ( ⁇ ') of the operating variables of the motor vehicle and / or the internal combustion engine as a function of these predefined input variables, and then the emissions as a function of these generated temporal Gradients are determined. That is, the machine learning system is initially trained by means of measured time courses to be able to generate realistic temporal courses.
  • the randomly selected input variables (z) actual time histories are then generated, for which emissions are then determined, for example using a suitable mathematical model such as, for example, a machine learning method or a physicochemical model. It may also be provided to measure the emissions on a real system in order to train, for example, said machine learning method.
  • the additional variables it is possible to generate profiles that correspond to the given properties.
  • the randomly selected input variables are determined by means of a method of statistical experimental design.
  • the invention relates to a computer program which is adapted to carry out one of the aforementioned methods, if it is on a Computer is running, a machine-readable storage medium on which this computer program is stored (this storage medium may of course be spatially distributed, eg distributed in parallel execution across multiple computers), and a device, in particular a monitoring unit, which is set up, one of these methods execute (for example, by playing the aforementioned computer program).
  • Figure 1 shows a structure of a motor vehicle
  • Figure 2 shows a device for determining the emissions
  • FIG. 3 shows by way of example a construction of a device for training the machine learning system
  • FIG. 4 shows by way of example a use of the machine learning system for determining emissions
  • FIG. 5 shows an exemplary construction of the machine learning system
  • FIG. 6 shows an alternative exemplary construction of the machine learning system.
  • Figure 1 shows an example of a structure of a motor vehicle (10).
  • the motor vehicle is driven by an internal combustion engine (20).
  • Combustion products produced during operation of the internal combustion engine (20) are conducted through an exhaust tract (30), which in particular comprises an exhaust gas purification system (40), for example a catalytic converter.
  • exhaust tract (30) which in particular comprises an exhaust gas purification system (40), for example a catalytic converter.
  • emissions (50) escape into the environment, in particular nitrogen oxides, soot particles and carbon dioxide.
  • Figure 2 shows an example of a structure of a device (200), with the emissions (50) of the motor vehicle (10) can be determined in practical driving.
  • the device (200) is a computer which comprises a machine-readable storage medium (210) on which a computer program (220) is stored. This computer program is set up to carry out one of the methods according to the invention, ie the computer program (220) contains instructions which cause the computer (200) to carry out the method according to the invention when the computer program (220) is executed
  • FIG. 3 shows by way of example a structure of a device for training the machine learning system (M).
  • the machine learning system (M) are supplied as input variables of measured time profiles (x) of operating variables of the motor vehicle (10) and / or of the internal combustion engine (20). These measured time profiles do not have to originate from the same motor vehicle, and can for example be stored in a database.
  • the machine learning system (M) generates therefrom an output quantity, namely either time histories ( ⁇ ') of the operating quantities or a result of discrimination (d).
  • the measured time courses (x) and the generated time courses ( ⁇ ') or alternatively the discrimination result (d) are fed to a learning unit (L), which uses, for example, by means of a gradient descent method the parameters (v, y, ⁇ , ⁇ , ⁇ , Y ) so that a cost function is optimized.
  • FIG. 4 shows by way of example a use of the machine learning system (M) for determining emissions (e).
  • the machine learning system (M) generates time profiles ( ⁇ ') of operating variables of the motor vehicle (10) and / or the internal combustion engine (20).
  • FIG. 5 shows in more detail an exemplary construction of the machine learning system (M).
  • FIG. 5a shows the structure how it can be used during training.
  • the machine learning system (M) comprises an encoder (K) and a decoder (D).
  • the coder (K) determines from the measured temporal progressions (x) and parameters (v, ⁇ ) fed to it quantities (z, q e (z
  • the decoder (D) can also be supplied with further variables (not shown).
  • the decoder (D) from these quantities (z, qe (z
  • FIG. 5b shows the structure as it can be used when generating generated time profiles ( ⁇ ').
  • a block (S) generates latent variables (z) according to a predefinable distribution. For example, a probability density is determined by means of a density estimator as a function of the latent variables z determined as shown in FIG. 5a, from which the block (S) now randomly draws a random sample.
  • These generated latent variables (z) are fed to the decoder (D), which generates the generated time profiles ( ⁇ ') as a function of parameters ( ⁇ , ⁇ ).
  • coders (K) and decoders (D) can, for example, form an auto-decoder, or implement a partial auto-encoder, or a sparse dictionary learning.
  • the decoder (D) includes a Gaussian process.
  • the encoder (K) determines the latent variables (z) as a function of parameters (v), and in addition to the parameters ( ⁇ ) characterizing the Gaussian process, this also applies during training Parameters (v) are varied such that a marginal probability (p (x
  • the encoder (K) is omitted and latent variables (z) are specified directly, such that the learning unit (L) adjusts these latent variables (z) in addition to the parameters ( ⁇ ) in such a way that a cost function which causes a reconstruction error between the measured time course (x) and the associated course generated from the selected latent variables (z) ( ⁇ ') is minimized.
  • FIG. 6 shows in more detail an alternative exemplary structure of the machine learning system (M).
  • FIG. 6a shows the structure how it can be used during training.
  • the machine learning system (M) comprises a first block (U) and a second block (H).
  • the first block (U) is parametrized by parameters (Y), the second block (H) by parameters ( ⁇ ).
  • a random number generator
  • R determines random numbers (or, as is often the case, pseudo-random numbers) r and delivers them to the second block (H).
  • the second block (H) further variables (not shown) are supplied, the characterizing parameters encode the second block (H) generated from the random numbers (r) and possibly the other variables depending on the parameters ( ⁇ ) each one generated time course ( ⁇ ').
  • the first block (U) is either a generated time course ( ⁇ ') or a measured time course (x) supplied. It is also possible for the first block (U) to be supplied with these two courses (x, x ') if the first block (U) has an internal selection mechanism (not shown), each of which has one of these two courses (x, x). x ').
  • the first block (U) is trained by adapting its behavior determining parameter (Y) to be able to differentiate as well as possible whether the variable supplied to it is a measured time course (x) or a generated time course ( ⁇ '). ).
  • the information as to whether this classification of the first block (U) is true or false is encoded in the discrimination result (d).
  • the first block (U) and the second block (H) are now trained alternately, the parameters (Y) of the first block (U) are trained so that the classification of the first block (U) is often correct and the parameters ( ⁇ ) of the second block (H) that the classification of the first block (U) is as often as possible wrong.
  • FIG. 6b shows the corresponding structure as it can be used to generate generated time profiles ( ⁇ ').
  • the random number generator (R) generates random numbers or pseudo-random numbers (r), and the second block (H) generates the generated temporal courses ( ⁇ ') depending on the further variables and, if necessary, on the parameters adapted in training ( ⁇ ). )

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Abstract

L'invention concerne un procédé permettant de déterminer des émissions (50) d'un véhicule à moteur (10) actionné par un moteur à combustion interne (20), dans un mode de conduite pratique (en anglais : real driving emissions = émissions en conditions de conduite réelles), un système d'apprentissage mécanique étant entraîné, au moyen de courbes temporelles mesurées de grandeurs de fonctionnement dudit véhicule à moteur (10) et/ou du moteur à combustion interne (20), à générer des courbes temporelles des grandeurs de fonctionnement, puis à déterminer les émissions en fonction de ces courbes temporelles générées.
EP18793567.1A 2017-10-16 2018-10-09 Calcul d'émissions de gaz d'échappement d'un véhicule à moteur Pending EP3698036A1 (fr)

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DE102017218476.0A DE102017218476A1 (de) 2017-10-16 2017-10-16 Verfahren und Vorrichtung zum Ermitteln von Emissionen
PCT/EP2018/077474 WO2019076685A1 (fr) 2017-10-16 2018-10-09 Calcul d'émissions de gaz d'échappement d'un véhicule à moteur

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JP2020537087A (ja) 2020-12-17
KR20200066348A (ko) 2020-06-09
US11078857B2 (en) 2021-08-03
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