Methodik Zur Effizienten Applikation Automatisierter Fahrfunktionen
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Methodik zur effizienten Applikation automatisierter Fahrfunktionen
Author: Fraikin, Nicolas
language: de
Publisher: KIT Scientific Publishing
Release Date: 2024-04-17
In der Arbeit wird eine Methodik zur effizienten Applikation automatisierter Fahrfunktionen vorgestellt. Die Methodik basiert auf einer Einschränkung des relevanten Parameterraums zur Optimierung des Funktionsverhaltens. Außerdem wird ein sensitivitätsbasiertes Optimierungsverfahren vorgestellt, welches optimale Lösungen mit hohem Reifegrad bereitstellt. Zur Erzielung kurzer Rechenzeiten wird ein hybrides Fahrzeugmodell vorgestellt, welches aus einem Einspurmodell und neuronalen Netz besteht. - The work introduces a method for an efficient calibration of automated driving functions. The method is based on an initial limitation of the relevant parameter space for the optimization of function behaviour. Furthermore, a sensitivity based optimization is presented, which provides optimal solution with a high maturity level. Finally, a hybrid vehicle model (based on a single track model and a neuronal network) is described, which enables a high accuracy and short simulation times.
Constrained-layer damping in hybrid fibre metal elastomer laminates and its tolerance to damage
Author: Jackstadt, Alexander
language: en
Publisher: KIT Scientific Publishing
Release Date: 2025-04-07
This work presents experimental, analytical and numerical methods for predicting structural behaviour as well as damping capabilities of hybrid fibre metal elastomer laminates (FMELs), particularly for the assessment and optimisation of constrained-layer damping (CLD) with regard to its tolerance to damage.
Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles with Federated Learning
Author: Thorgeirsson, Adam Thor
language: en
Publisher: KIT Scientific Publishing
Release Date: 2024-09-03
In this work, an extension of the federated averaging algorithm, FedAvg-Gaussian, is applied to train probabilistic neural networks. The performance advantage of probabilistic prediction models is demonstrated and it is shown that federated learning can improve driving range prediction. Using probabilistic predictions, routing and charge planning based on destination attainability can be applied. Furthermore, it is shown that probabilistic predictions lead to reduced travel time.