Stefan Scheubner
Porsche
6 Papers
9 Citations
Stefan Scheubner is an academic researcher from Porsche. The author has contributed to research in topics: Range anxiety & Driving range. The author has an hindex of 3, co-authored 6 publications. Previous affiliations of Stefan Scheubner include Porsche Automobil Holding SE & Karlsruhe Institute of Technology.
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Papers
Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles With Federated Learning
Adam Thor Thorgeirsson,Stefan Scheubner,Sebastian Funfgeld,Frank Gauterin +3 more
- 11 Mar 2021
TL;DR: In this article, an extension of the federated averaging algorithm is applied to learn probabilistic neural networks and linear regression models in a communication-efficient and privacy-preserving manner.
An Investigation Into Key Influence Factors for the Everyday Usability of Electric Vehicles
Adam Thor Thorgeirsson,Stefan Scheubner,Sebastian Funfgeld,Frank Gauterin +3 more
- 16 Oct 2020
TL;DR: One of the key findings is that battery capacities beyond 100 kWh are not feasible and the importance of an accurate range estimation algorithm and a dense network of high-performance charging points for everyday usability is stated.
Validation of range estimation for electric vehicles based on recorded real-world driving data
Patrick Petersen,Jacob Langner,Stefan Otten,Eric Sax,Stefan Scheubner,Moritz Vaillant,Sebastian Funfgeld,F. Porsche +7 more
- 01 Jan 2019
TL;DR: In this article, the authors proposed a range estimation method for battery electric vehicles, which can reduce the range anxiety of battery electric vehicle drivers and reduce greenhouse gas emissions, but short maximum range and missing charging infrastructure limits user acceptance.
6
Training and Validation Methodology for Range Estimation Algorithms
Patrick Petersen,Adam Thor Thorgeirsson,Adam Thor Thorgeirsson,Stefan Scheubner,Stefan Otten,Frank Gauterin,Eric Sax +6 more
- 12 Oct 2019
TL;DR: This paper proposes a novel methodology for the development and validation of range estimation algorithms based on machine learning validation approaches that considers the evaluation of driver-specific and driver-unspecific performance.
5
A Stochastic Range Estimation Algorithm for Electric Vehicles Using Traffic Phase Classification
TL;DR: Both the superiority of stochastic algorithms over deterministic predictions and the improvement of predictive performance by evaluating explicit traffic phases can be shown.