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DOI: 10.4230/LIPIcs.SEA.2022.15

Stochastic Route Planning for Electric Vehicles

Authors: Payas Rajan and Chinya V. Ravishankar

Published in: LIPIcs, Volume 233, 20th International Symposium on Experimental Algorithms (SEA 2022)

Abstract

Electric Vehicle routing is often modeled as a generalization of the energy-constrained shortest path problem, taking travel times and energy consumptions on road network edges to be deterministic. In practice, however, energy consumption and travel times are stochastic distributions, typically estimated from real-world data. Consequently, real-world routing algorithms can make only probabilistic feasibility guarantees. Current stochastic route planning methods either fail to ensure that routes are energy-feasible, or if they do, have not been shown to scale well to large graphs. Our work bridges this gap by finding routes to maximize on-time arrival probability and the set of non-dominated routes under two criteria for stochastic route feasibility: 𝔼-feasibility and p-feasibility. Our 𝔼-feasibility criterion ensures energy-feasibility in expectation, using expected energy values along network edges. Our p-feasibility criterion accounts for the actual distribution along edges, and keeps the stranding probability along the route below a user-specified threshold p. We generalize the charging function propagation algorithm to accept stochastic edge weights to find routes that maximize the probability of on-time arrival, while maintaining 𝔼- or p-feasibility. We also extend multi-criteria Contraction Hierarchies to accept stochastic edge weights and offer heuristics to speed up queries. Our experiments on a real-world road network instance of the Los Angeles area show that our methods answer stochastic queries in reasonable time, that the two criteria produce similar routes for longer deadlines, but that 𝔼-feasibility queries can be much faster than p-feasibility queries.

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Payas Rajan and Chinya V. Ravishankar. Stochastic Route Planning for Electric Vehicles. In 20th International Symposium on Experimental Algorithms (SEA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 233, pp. 15:1-15:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{rajan_et_al:LIPIcs.SEA.2022.15,
  author =	{Rajan, Payas and Ravishankar, Chinya V.},
  title =	{{Stochastic Route Planning for Electric Vehicles}},
  booktitle =	{20th International Symposium on Experimental Algorithms (SEA 2022)},
  pages =	{15:1--15:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-251-8},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{233},
  editor =	{Schulz, Christian and U\c{c}ar, Bora},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2022.15},
  URN =		{urn:nbn:de:0030-drops-165497},
  doi =		{10.4230/LIPIcs.SEA.2022.15},
  annote =	{Keywords: Stochastic Routing, Electric Vehicles, Route Planning Algorithms}
}

Document

DOI: 10.4230/OASIcs.ATMOS.2021.11

Robustness Generalizations of the Shortest Feasible Path Problem for Electric Vehicles

Authors: Payas Rajan, Moritz Baum, Michael Wegner, Tobias Zündorf, Christian J. West, Dennis Schieferdecker, and Daniel Delling

Published in: OASIcs, Volume 96, 21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021)

Abstract

Electric Vehicle routing is often modeled as a Shortest Feasible Path Problem (SFPP), which minimizes total travel time while maintaining a non-zero State of Charge (SoC) along the route. However, the problem assumes perfect information about energy consumption and charging stations, which are difficult to even estimate in practice. Further, drivers might have varying risk tolerances for different trips. To overcome these limitations, we propose two generalizations to the SFPP; they compute the shortest feasible path for any initial SoC and, respectively, for every possible minimum SoC threshold. We present algorithmic solutions for each problem, and provide two constructs: Starting Charge Maps and Buffer Maps, which represent the tradeoffs between robustness of feasible routes and their travel times. The two constructs are useful in many ways, including presenting alternate routes or providing charging prompts to users. We evaluate the performance of our algorithms on realistic input instances.

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Payas Rajan, Moritz Baum, Michael Wegner, Tobias Zündorf, Christian J. West, Dennis Schieferdecker, and Daniel Delling. Robustness Generalizations of the Shortest Feasible Path Problem for Electric Vehicles. In 21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021). Open Access Series in Informatics (OASIcs), Volume 96, pp. 11:1-11:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{rajan_et_al:OASIcs.ATMOS.2021.11,
  author =	{Rajan, Payas and Baum, Moritz and Wegner, Michael and Z\"{u}ndorf, Tobias and West, Christian J. and Schieferdecker, Dennis and Delling, Daniel},
  title =	{{Robustness Generalizations of the Shortest Feasible Path Problem for Electric Vehicles}},
  booktitle =	{21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021)},
  pages =	{11:1--11:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-213-6},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{96},
  editor =	{M\"{u}ller-Hannemann, Matthias and Perea, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2021.11},
  URN =		{urn:nbn:de:0030-drops-148807},
  doi =		{10.4230/OASIcs.ATMOS.2021.11},
  annote =	{Keywords: Electric Vehicles, Route Planning}
}

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Stochastic Route Planning for Electric Vehicles

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Robustness Generalizations of the Shortest Feasible Path Problem for Electric Vehicles

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