Robert Boschgmbh

Volker Imhof

Ernst Kloppenburg

Heiner Markert

Thomas Kruse

Holger Ulmer

Felix Streichert

Stefan Angermaier

Rene Diener

Axel Aue

Tobias Lang

Martin Schiegg

Nico Bannow

Christoph Zimmer

Jochen Braun

Ulrich Schulmeister

Christian Daniel

The Duy Nguyen-Tuong

Edgar Klenske

Sebastian Gerwinn

Michael Saetzler

Bastian Bischoff

Thomas Richardsen

Thorsten Huber

Harald Stuhler

Sriganesh Sriram

A device and method are disclosed for operating a fuel cell system having a fuel cell stack. Data are provided that map input variables of the fuel cell system and a position of a cell of the fuel cell stack on a voltage of the cell. A model is trained to map the input variables of the fuel cell system and the position of a cell of the fuel cell stack on a probability distribution for a prediction of a voltage of the cell. Instantaneous input variables of the fuel cell system are determined. A probability for the voltage of the cell or for a total voltage of the fuel cell stack is determined for a cell of the fuel cell stack based on the input variables using the model based on the probability distribution. A state of the fuel cell system is determined based on the probability.

Device and computer-implemented method for determining a state of a fuel cell system

A method is provided for ascertaining a NOconcentration and an NHslip downstream from an SCR catalytic converter of an internal combustion engine of a vehicle. State variables of an internal combustion engine as first input variables and an updated NHfill level of the SCR catalytic converter as a second input variable cooperate with at least one machine learning algorithm or at least one stochastic model. The at least one machine learning algorithm or at least one stochastic model calculates the NOconcentration and the NHslip downstream from the SCR catalytic converter as a function of the first input variables and the second input variables and output the same as output variables.

Method for ascertaining a NOconcentration and a NHslip downstream from an SCR catalytic converter

A method for automatically generating a machine learning system which ascertains as a function of an input variable time series an output variable time series approximating an actual output variable time series, the machine learning system ascertaining a value of the output variable assigned to the predefinable point in time as a function of input and output variable values at the points in time, which are in a predefinable time interval, prior to a predefinable point in time, only a subset of the values of the input variable within the interval and of the values of the output variable being incorporated when ascertaining the output variable assigned to the predefinable point in time, the subset being selected so that it includes available input variable values and the output variable values assigned to the points in time within the interval, which are in a predefinable equidistant selection raster within the interval.

Method and device for generating a machine learning system and virtual sensor device

A method for generating an allowed input data trajectory for a physical system to be tested or measured, including providing an input data trajectory in an input data space; determining an allowed operating range; and replacing at least one segment of the provided input data trajectory, which is outside of the allowed operating range, by a trajectory segment within the allowed operating range, in order to obtain the allowed input data trajectory.

Method and device for generating allowed input data trajectories for a testing system

A control device in a vehicle includes a unit for calculating, during operation of the vehicle, on the basis of at least one input variable ascertained during operation, at least one output variable for a control system of functions of the vehicle. The control device performs the calculation of the output variables using a Bayesian regression of training values ascertained, before operation, for the output variable and the input variable.

Control device and method for calculating an output parameter for a controller

A method is provided for populating a function for a control unit with data, in which method measurements are performed on a system at different measuring points on a test stand, and a global data-based model is set up based on the obtained measured values, and virtual measurements which simulate real measurements on the test stand are carried out on the global data-based model, and uncertainties for virtual measured values of the virtual measurements are determined from the global data-based model, the uncertainties of the virtual measured values being taken into account when populating the function for the control unit with data.

Method for setting up a functionality for a control unit

A method for optimizing characteristics maps for the control of an internal combustion engine in which a model of the internal combustion engine is first formed by measurements on the internal combustion engine. In the process, a dependency of output variables of the internal combustion engine on the characteristics maps is illustrated. In the optimization, the dependency of the output variables of the internal combustion engine on the characteristics maps and the smoothing of the characteristics maps are optimized simultaneously.

Method for optimizing characteristics map

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Kornwestheim, Germany

Volker Imhof