Robert Boschgmbh

Sebastian Gerwinn

Martin Schiegg

Christoph Zimmer

Muhammad Bilal Zafar

Volker Imhof

Jochen Braun

Melih Kandemir

Barbara Rakitsch

Sriganesh Sriram

Roman Dominik Kilgus

Andreas Look

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 for determining a state of a transmission for a vehicle includes providing an input for a first generative model depending on a route information, a vehicle speed, a probabilistic variable, and an output of a second physical model, and determining an output of the first model characterizing the state of the transmission in response to the input for the first model. The first model includes a first layer trained to map input to an intermediate state and a second layer trained to map the intermediate state to the state of the transmission depending on the output of the second model. The method includes providing an input for the second physical model depending on at least one vehicle state and/or the route information, and determining an output of the second model in response to the input for the second model. The output of the second model characterizes limit(s) for the intermediate state.

Device, method and machine learning system for determining a state of a transmission for a vehicle

A computer-implemented method of training a model for making time-series predictions of a computer-controlled system. The model uses a stochastic differential equation (SDE) comprising a drift component and a diffusion component. The drift component has a predefined part representing domain knowledge, that is received as an input to the training; and a trainable part. When training the model, values of the set of SDE variables at a current time point are predicted based on their values at a previous time point, and based on this, the model is refined. In order to predict the values of the set of SDE variables, the predefined part of the drift component is evaluated to get a first drift, and the first drift is combined with a second drift obtained by evaluating the trainable part of the drift component.

Making time-series predictions of a computer-controlled system

A device and a method for operating a test stand. A set of measurements of input variables of a system model of a component of a machine is provided. An optimization problem is defined using a set of measurements of input variables. A gradient for solving the optimization problem is determined as a function of the set of measurements. A solution to the optimization problem, which defines a design for input data for the test stand for a measurement on the component, is determined as a function of the gradient. A measurement of output data is acquired on the component on the test stand as a function of the input data. Pairs of training input data and training output data are determined as a function of the input data and the measurement of output data. The system model for the component is trained as a function of the pairs.

Device and method for operating a test stand

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Leonberg, Germany

Sebastian Gerwinn