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Diana Schramm, B.Sc.

Fakultät Angewandte Informatik

Mitarbeitende

Projektmitarbeiterin

ITC2+ 1.07

0991/3615-636

Sortierung:
Zeitschriftenartikel
  • Peter Sperber
  • Katrin Juds
  • Stefan Schuster
  • Ariane Hartmann
  • Diana Schramm

Entwicklung und Einsatz des Optimierten Reichweitenmodells im Verbundprojekt E-WALD.

In: Zeitschrift für die gesamte Wertschöpfungskette Automobilwirtschaft (ZfAW) , pg. 53-59

(2015)

Beitrag in Sammelwerk/Tagungsband
  • Markus Eider
  • Diana Schramm
  • Andreas Berl
  • R. Basmadjian
  • H. Meer
  • S. Klingert
  • T. Schulze
  • F. Kutzner
  • C. Kacperski
  • M. Štolba

Seamless Electromobility.

New York NY: ACM pg. 316-321

DOI: 10.1145/3077839.3078461

(2017)

Beitrag in Sammelwerk/Tagungsband
  • Christian Kluge
  • Stefan Schuster
  • Diana Schramm

Statistics instead of Stopover.

  • In:
  • M. Geiger
  • A. Fügenschuh
  • A. Fink
  • Range Predictionc for Electric Vehicles.

Springer pg. 51-56

(2017)

Beitrag in Sammelwerk/Tagungsband
  • Nicki Bodenschatz
  • Diana Schramm
  • Markus Eider
  • Andreas Berl

Classification of Electric Vehicle Fleets Considering the Complexity of Fleet Charging Schedules.

  • [Status: Presented].

    • New York, NY: ACM

    (2018)

    Beitrag in Sammelwerk/Tagungsband
    • Markus Eider
    • Diana Schramm
    • Nicki Bodenschatz
    • Andreas Berl
    • P. Danner
    • H. Meer

    A Novel Approach on Battery Health Monitoring.

    (2018)

    Zeitschriftenartikel
    • Diana Schramm
    • Nicki Bodenschatz
    • Andreas Berl

    Usage Profiling in Electric Vehicles.

    In: Bavarian Journal of Applied Sciences (vol. 4) , pg. 342-353

    (2018)

    DOI: 10.25929/bjas.v4i1.52

    Abstract anzeigen

    In the overall effort of reducing CO2 emissions, the significance of alternative drive engines is growing. The transition from combustion engine vehicles to electric vehicles is high on the political agendas, with governments providing extensive funding to promote electric mobility. However, there are still challenges that hamper the dissemination of electric vehicles. One of those challenges is the limited range and the resulting range anxiety. Displayed vehicle range data contribute to this, as they are relatively inaccurate and might vary quite strongly during individual trips. This problem could be addressed by personalizing the range display according to the driving style of the current driver. Driver assistance services, like distance control, are becoming increasingly personalized nowadays, however, they are predominantly designed for internal combustion engine vehicles. In this paper, relevant input parameters for classifying the driving styles of electric vehicle users are identified. Furthermore, a system based on real-life driving data is developed to determine the driving style. Real-life driving data were collected in experiments and used to profile the driving style by means of fuzzy logic. Based on the results, an approach for a realistic classification of driving styles of electric vehicle users is discussed.
    Vortrag
    • Nicki Bodenschatz
    • Markus Eider
    • Diana Schramm
    • Andreas Berl

    Optimierte Ladeplanung von Elektrofahrzeugflotten. Posterpräsentation.

    • Technische Hochschule Deggendorf.

    Deggendorf 08.03.2018.

    (2018)