Christof Nitsche

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Fuel cell vehicles and fuel cell research is one of the newer areas in automotive technology. This paper describes an approach that utilizes artificial neural networks to alleviate the task of onboard diagnostics for fuel cell vehicles. The basic idea is an online learning scenario that trains a power train model with every-day driving data; this model can(More)
Component failures in hybrid electric vehicles (HEV) can cause high warranty costs for car manufacturers. Hence, in order to (1) predict whether a component of the hybrid power-train of a HEV is faulty, and (2) to identify loads related to component failures, we train several random forest variants on so-called load spectrum data, i.e., the state-of-the-art(More)
To be able to optimize the dimensioning of the power-train of a hybrid electric vehicle, engineers have to find relationships between stresses of the power-train and failures of its components. In this paper, we apply the machine learning technique random forest to a heterogeneous dataset consisting of so-called "load spectrum" data resulting from(More)
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