The shortest path problem revisited: optimal routing for electric vehicles

  • Authors:

  • Andreas Artmeier;Julian Haselmayr;Martin Leucker;Martin Sachenbacher

  • Affiliations:

  • Technische Universität München, Department of Informatics, Garching, Germany;Technische Universität München, Department of Informatics, Garching, Germany;Technische Universität München, Department of Informatics, Garching, Germany;Technische Universität München, Department of Informatics, Garching, Germany

  • Venue:
  • KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
  • Year:
  • 2010

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Abstract

Electric vehicles (EV) powered by batteries will play a significant role in the road traffic of the future. The unique characteristics of such EVs - limited cruising range, long recharge times, and the ability to regain energy during deceleration - require novel routing algorithms, since the task is now to determine the most economical route rather than just the shortest one. This paper proposes extensions to general shortestpath algorithms that address the problem of energy-optimal routing. Specifically, we (i) formalize energy-efficient routing in the presence of rechargeable batteries as a special case of the constrained shortest path problem (CSPP) with hard and soft constraints, and (ii) present an adaption of a general shortest path algorithm (using an energy graph, i.e., a graph with a weight function representing the energy consumption) that respects the given constraints and has a worst case complexity of O(n3). The presented algorithms have been implemented and evaluated within a prototypic navigation system for energy-efficient routing.