An Approach Towards Service Infrastructure Optimization for Electromobility

Chapter
First Online:
Part of the Lecture Notes in Mobility book series (LNMOB)

Abstract

Electric mobiles are one of many possible answers regarding the increasing costs of fossil fuels and carbon emission reduction targets. The main contraint is that the new technology must cover the users’ mobility needs which are still rising. Due to the technical restrictions of electric vehicles, a switch to electromobility is bound to the need for a sufficient charging infrastructure taking into account current and future mobility behavior as well as the characteristics of electric vehicles. This paper explains a methodology to design and assess the charging infrastructure layout without having a significant amount of electric vehicles available. This approach includes the use of the simulation tool TrIAS (Transportation infrastructure assessment by simulation). The basis is a planning model that is able to represent concepts and parameters like users’ mobility patterns, regional street layout, available parking infrastructure as well as the e-car and its technical characteristics like range or battery capacity itself. This model is the input for a simulation-based analysis. The approach is applied in the region Bremen/Oldenburg to analyze the requirements towards a sufficient charging infrastructure for electric vehicles.

Keywords

Electric Vehicle Simulation Tool Planning Model Mobility Pattern Facility Location Problem 
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.
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References

  1. Arnold H, Kuhnert F, Kurtz R, Bauer W (2010) Elektromobilität: Herausforderungen für Industrie und öffentliche Hand. Accessed 23 Nov 2011Google Scholar
  2. Azarmand Z (2009) Location allocation problem. In Farahani RZ (ed.) Facility location: concepts, models, algorithms and case studies. Physica-Verlag, Berlin, pp 93–109Google Scholar
  3. Balmer M, Axhausen K, Nagel K (2006) Agent-based demand-modeling framework for large-scale microsimulations. J Transport Res Rec 1985:125Google Scholar
  4. Choy MC, Srinivasan D, Cheu RL (2003) Cooperative, hybrid agent architecture for real-time traffic signal control. IEEE Trans Syst Man Cybern Part A Syst Humans 33:597Google Scholar
  5. Daneshzand F (2009) Multifacility location problem. In Farahani RZ (ed) Facility location: concepts, models, algorithms and case studies. Physica-Verlag, Berlin, 69–92Google Scholar
  6. France J, Ghorbani A (2003) A multiagent system for optimizing urban traffic. In IEEE/WIC international conference on intelligent agent technology IAT 2003, pp 411–414)Google Scholar
  7. Gringmuth C, Liedtke G, Geweke S, Rothengatter W (2000) Impacts of intelligent information systems on transport and the economy—the micro based modelling system OVID. In: Advances in modeling, optimization and management of transportation processes and systems: theory and practice—10th meeting of the EURO working group transportation (EWGT)Google Scholar
  8. Hernándes JZ, Ossowski S, García-Serrano A (2002) Multiagent architectures for intelligent traffic management systems. Transport Res Part C Emerg Technol 10:473Google Scholar
  9. Hoberg P, Leimeister S, Jehle H, Krcmar H (2010) Elektromobilität 2010: Grundlagenstudie zu Voraussetzungen der Entwicklung von Elektromobilität in der Modellregion München. fortiss GmbH. http://www.fortiss.org/fileadmin/user_upload/FB3/Grundlagenstudie_Elektromobilitaet2010_fortiss_final.pdf. Accessed 23 Nov 2011
  10. Hunecke M, Schubert S, Zinn F (2004) Mobilitätsbedürfnisse und Verkehrsmittelwahl im Nahverkehr. Ein einstellungsbasierter Zielgruppenansatz. Internationales Verkehrswesen 57(1/2):26–33Google Scholar
  11. Infas, DLR (2010) Mobilität in Deutschland 2008: Ergebnisbericht. Struktur—Aufkommen—Emissionen—Trends. http://www.mobilitaet-in-deutschland.de/pdf/MiD2008_Abschlussbericht_I.pdf. Accessed 23 Nov 2011
  12. Junges R, Bazzan ALC (2008) Evaluating the performance of DCOP algorithms in a real world, dynamic problem. In: AAMAS’08: proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, Vol 2. International Foundation for Autonomous Agents and Multiagent Systems, RichlandGoogle Scholar
  13. Klügl F (2001) Multiagentensimulation: Konzepte, Werkzeuge, Anwendungen. Addison-Wesley, MünchenGoogle Scholar
  14. Kosonen I (2003) Multi-agent fuzzy signal control based on real-time simulation. Transport Res Part C Emerg Technol 11:389Google Scholar
  15. Moradi E (2009) Single facility location problem. In Farahani RZ (ed) Facility location: concepts, models, algorithms and case studies. Physica-Verlag, Berlin, pp 37–68Google Scholar
  16. Nagel K, Schreckenberg M (1992) A cellular automaton model for freeway traffic. J de Physique I 12:2221Google Scholar
  17. Nunes L, Oliveira E (2004) Learning from multiple sources. In: AAMAS-2004—proceedings of the 3rd international joint conference on autonomous agents and multi agent systems, IEEE Computer Society, pp 1106–1113Google Scholar
  18. de Oliveira D, Ferreira P, Bazzan A, Klügl F (2004) A swarm-based approach for selection of signal plans in urban scenarios. In Dorigo M, Birattari M, Blum C, Gambardella L, Mondada F, Stützle T (eds) Ant colony optimization and swarm intelligence, Vol 3172. Springer, Berlin, pp 143–156 (Lecture Notes in Computer Science)Google Scholar
  19. Oliver Wyman (2009) Oliver Wyman-Studie „Elektromobilität 2025: Powerplay beim Elektrofahrzeug. http://www.oliverwyman.com/de/pdf-files/ManSum_E-Mobility_2025.pdf. Accessed 23 Nov 2011
  20. OpenStreetMap Foundation (2011) http://www.openstreetmap.org/. Accessed 23 Nov 2011
  21. Ossowski S, García-Serrano A (1999) Social structure in artificial agent societies: implications for autonomous problem-solving agents. In Proceedings of the 5th international workshop on intelligent agents V, agent theories, architectures, and languages ATAL’98. Springer, London, pp 133–148Google Scholar
  22. Rochner F, Prothmann H, Branke J, Müller-Schloer C, Schmeck H (2006) An organic architecture for traffic light controllers. In Hochberger C, Liskowsky R (eds) Informatik 2006—Informatik für Menschen, Vol 93. Köllen Verlag, pp 120–127 (Lecture notes in informatics (LNI))Google Scholar
  23. Salhi S, Gamal MDH (2003) A genetic algorithm based approach for the uncapacitated continuous location–allocation problem. Ann Oper Res 123:1Google Scholar
  24. Spath D, Bauer W, Rohfuss F, Voigt S, Rath K (2010) Strukturstudie BWe mobil—Baden-Württemberg auf dem Weg in die Elektromobilität. Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO. http://www.iao.fraunhofer.de/images/studien/strukturstudie-bwe-mobil.pdf. Accessed 23 Nov 2011
  25. Steierwald G (2005) Stadtverkehrsplanung: Grundlagen, Methoden, Ziele, 2nd edn. Springer, BerlinGoogle Scholar
  26. Vasirani M (2009) Vehicle-centric coordination for urban road traffic management: a market-based multiagent approach. Diss. Universidad Rey Juan Carlos, Madrid. http://hdl.handle.net/10115/5146
  27. VDE (2010) Smart energy 2020: Vom smart metering zum smart grid. Verband der Elektrotechnik Elektronik Informationstechnik e.VGoogle Scholar
  28. Wiering M (2000) Multi-agent reinforcement learning for traffic light control. In: Proceedings of the 17th international conference on machine learning. Morgan Kaufmann Publishers,Burlington, pp 1151–1158Google Scholar
  29. Wietschel M, Kley F, Dallinger D (2009) Eine Bewertung der Ladeinfrastruktur für Elektrofahrzeuge. ZfAW. Zeitschrift für die gesamte Wertschöpfungskette Automobilwirtschaft 2009(3):33–41Google Scholar
  30. Zarinbal M (2009) Distance functions in location problems. In Farahani RZ (ed) Facility location: concepts, models, algorithms and case studies. Physica-Verlag, Berlin, pp 5–17Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1. R&D Division TransportationOFFIS - Institute for Information TechnologyOldenburgGermany

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