EasyEV: Monitoring and Querying System for Electric Vehicle Fleets Using Smart Car Data

  • Gregor JosséEmail author
  • Matthias Schubert
  • Ludwig Zellner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)


Electric vehicles (EVs) have great potential as a modern mobility concept. Electricity already relies on a broad infrastructure and is available anywhere in developed countries. Furthermore, EVs are emmission-free which makes them the preferable form of individual transportation in urban areas where air pollution is often alarmingly high. However, operating EVs has several drawbacks compared to common combustion engine cars. The range of most EVs is rarely above 150 km, and when running out of energy, recharging an EV usually takes up to several hours. In order to benefit from the advantages of EVs without being afflicted with the disadvantages, it is advisable to rely on the support from smart systems for trip and charge planning. In the project Shared E-Fleet, the shared use of a fleet of electric cars by a heterogeneous group of drivers is examined. In the presented demo, we introduce a spatio-temporal query system which was developed to support drivers and fleet managers alike. For the driver, the system provides assistance to keep in range of charging stations and provides routing alternatives to a specified destination. For the fleet manager, the system incorporates real-time information to identify possible delays or battery drainages and thereby detect deviations from the fleet schedule to allow for early rescheduling.


Cost Vector Fleet Manager Expected Delay Traffic Delay Recorded Trajectory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ehrgott, M., Gandibleux, X.: A survey and annotated bibliography of multiobjective combinatorial optimization. OR-Spektrum 22, 425–460 (2000)zbMATHMathSciNetCrossRefGoogle Scholar
  2. 2.
    Jossé, G., Schmid, K.A., Schubert, M.: Probabilistic resource route queries with reappearance. In: EDBT 15, pp. 445–456 (2015)Google Scholar
  3. 3.
    Kriegel, H.P., Renz, M., Schubert, M.: Route skyline queries: a multi-preference path planning approach. ICDE 10, pp. 261–272 (2010)Google Scholar
  4. 4.
    Newson, P., Krumm, J.: Hidden markov map matching through noise and sparseness. In: ACM SIGSPATIAL GIS 09, pp. 336–343 (2009)Google Scholar
  5. 5.
    Shekelyan, M., Jossé, G., Schubert, M.: Linear path skylines in multicriteria networks. In: ICDE 15, pp. 459–470 (2015)Google Scholar
  6. 6.
    Shekelyan, M., Jossé, G., Schubert, M.: Paretoprep: efficient lower bounds for path skylines and fast path computation. In: SSTD 15 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Gregor Jossé
    • 1
    Email author
  • Matthias Schubert
    • 1
  • Ludwig Zellner
    • 1
  1. 1.Institute for InformaticsLudwig-Maximilians-University MunichMunichGermany

Personalised recommendations