Creative Commons Attribution 3.0 Unported license
In route planning for electric vehicles (EVs), consumption profiles are a functional representation of optimal energy consumption between two locations, subject to initial state of charge. Efficient computation of profiles is a relevant problem on its own, but also a fundamental ingredient to many route planning approaches for EVs. In this work, we show that the complexity of a profile is at most linear in the graph size. Based on this insight, we derive a polynomial-time algorithm for the problem of finding an energy-optimal path between two locations that allows stops at charging stations. Exploiting efficient profile search, our approach also allows partial recharging at charging stations to save energy. In a sense, our results close the gap between efficient techniques for energy-optimal routes (based on simpler models) and NP-hard time-constrained problems involving charging stops for EVs. We propose a practical implementation, which we carefully integrate with Contraction Hierarchies and A* search. Even though the practical variant formally drops correctness, a comprehensive experimental study on a realistic, large-scale road network reveals that it always finds the optimal solution in our tests and computes even long-distance routes with charging stops in less than 300 ms.
@InProceedings{baum_et_al:LIPIcs.SEA.2017.19,
author = {Baum, Moritz and Sauer, Jonas and Wagner, Dorothea and Z\"{u}ndorf, Tobias},
title = {{Consumption Profiles in Route Planning for Electric Vehicles: Theory and Applications}},
booktitle = {16th International Symposium on Experimental Algorithms (SEA 2017)},
pages = {19:1--19:18},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-036-1},
ISSN = {1868-8969},
year = {2017},
volume = {75},
editor = {Iliopoulos, Costas S. and Pissis, Solon P. and Puglisi, Simon J. and Raman, Rajeev},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2017.19},
URN = {urn:nbn:de:0030-drops-76088},
doi = {10.4230/LIPIcs.SEA.2017.19},
annote = {Keywords: electric vehicles, charging station, shortest paths, route planning, profile search, algorithm engineering}
}