Robert Boschgmbh

Frank Kowol

Konrad Groh

Roland Norden

Jens-Holger Barth

Erik Tonner

Andreas Hopf

Mona Meister

Matthias Woehrle

Eberhard Schnaibel

Erich Junginger

Thomas Moser

Johannes Kanters

Reinhard Hein

A computer-implemented method for training a data-based time determining model for determining an opening or closing time of an injection valve based on a sensor signal. The method includes: providing an unlabeled analysis point time series by sampling the sensor signal of a sensor of the injection valve; training the data-based time determining model to assign a time specification which represents a specific opening or closing duration to an analysis point time series, the training process being carried out using a first shifting function to time-shift the analysis point time series and a second shifting function in order to time-shift the time specification. A consistency loss function is used for the training process.

Method and device for training a data-based time determining model for determining an opening or closing time of an injection valve using a machine learning method

A method for operating an injection valve by determining an opening or closing time of the injection valve based on a sensor signal. The method includes: providing an evaluation point time series by sampling a sensor signal of a sensor of the injection valve; using a non-linear data-based first sub-model to obtain a first output vector based on the evaluation point time series, wherein each element of the first output vector is associated with a specific time; using a linear, data-based second sub-model to obtain a second output vector based on the evaluation point time series, wherein each element of the second output vector is associated with a specific time; limiting the time determined by the first output vector depending on the second output vector in order to obtain the opening or closing time.

Method and device for operating a fuel injection valve with the aid of machine learning methods

A method for training a data-based evaluation model to determine an opening or closing time of an injection valve based on a sensor signal. The method includes: measuring an operation of the injection valve in order to determine at least one sensor signal and an associated opening or closing time; sampling the sensor signal at a sampling rate in order to obtain a sensor signal time series with sensor signal values; determining a plurality of training data sets by assigning a plurality of evaluation point time series generated from a sensor signal time series to the opening or closing time associated with the sensor signal, wherein the evaluation point time series has a lower temporal resolution than the sensor signal time series; training the data-based evaluation model depending on the determined training data sets.

Method and device for operating a fuel injection valve

A method for operating an injection valve by ascertaining an opening time and/or closing time of the injection valve on the basis of a sensor signal. The method includes: providing an analysis point time series by sampling a sensor signal of a sensor of the injection valve; using a nonlinear, data-based first submodel in order to obtain a first model output on the basis of the analysis point time series; using a linear, data-based second submodel in order to obtain a second model output on the basis of the analysis point time series; ascertaining the opening time and/or closing time as a function of the first and second model outputs.

Method and apparatus for operating a fuel injection valve with the aid of machine learning methods

A computer-implemented method for ascertaining a closure point in time of an injector of an internal combustion engine using a classifier. The method includes: ascertaining a time series of input signals, each corresponding to a point in time within the time series, and each characterizing a deformation of the injector; ascertaining a plurality of first values using the classifier based on the time series, in each case a first value corresponding to a point in time of the time series, and the first value characterizing a probability that the closure point in time of the injector matches the point in time; ascertaining a plurality of second values, each being a sum of neighboring first values, of a first value and the first value, the second value corresponding to the point in time to which the first value corresponds; ascertaining the closure point in time based on the largest second value.

Method and device for ascertaining a closure point in time of an injector of an internal combustion engine with the aid of a machine learning system

A procedure to calculate the Lambda value with a wideband Lambda sensor of an internal combustion engine of a motor vehicle is thereby characterized, in that from the measured pumping electricity and the sensitivities of the wideband Lambda sensor as well as the gas concentration ratios, in the lean operation an oxygen concentration and in the rich operation an oxygen deficit are determined and from these respectively a conclusion is drawn about the Lambda value using the Pischinger Formula.

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Ludwigsburg, Germany

Frank Kowol