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PerioClust: A Simple Hierarchical Agglomerative Clustering Approach Including Constraints Read chapter Read first chapter

2021 | OriginalPaper | Chapter

A Fast Electric Vehicle Planner Using Clustering

Authors : Jaël Champagne Gareau, Éric Beaudry, Vladimir Makarenkov

Published in: Data Analysis and Rationality in a Complex World

Publisher: Springer International Publishing

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Abstract

Over the past few years, several studies have considered the problem of Electric Vehicle Path Planning with intermediate recharge (EVPP-R) that consists of finding the shortest path between two given points by traveling through one or many charging stations, without exceeding the vehicle’s range. Unfortunately, the exact solution to this problem has a high computational cost. Therefore, speedup techniques are generally necessary (e.g., contraction hierarchies). In this paper, we propose and evaluate a new fast and intuitive graph clustering technique, which is applied on a real map with charging station data. We show that by grouping nearby stations, we can reduce the number of stations considered by a factor of 13 and increase the speed of computation by a factor of 35, while having a very limited trade-off increase, of less than \(1\%\), on the average journey duration time.

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Footnotes
1
\({{\,\mathrm{d}\,}}(A, B)\) is the distance in the graph between A and B.
 
2
For the sake of simplicity, \(c_i\) denotes both the set of stations in the cluster and the center node.
 
Literature
Baouche, F., Billot, R., Trigui, R., El Faouzi, N.E.: Electric vehicle green routing with possible en-route recharging. In: International Conference on Intelligent Transportation Systems (ITSC), pp. 2787–2792 (2014)
Champagne Gareau, J., Beaudry, É., Makarenkov, V.: Planification d’itinéraires quasi-optimaux pour un véhicule électrique en considérant le regroupement de bornes de recharge et leur probabilité d’occupation. In: XXV-émes Rencontres de la Société Francophone de Classification (SFC2018), pp. 5–8. Paris, France (2018)
Champagne Gareau, J., Beaudry, É., Makarenkov, V.: An efficient electric vehicle path-planner that considers the waiting time. In: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, Chicago, United States (2019)
Geisberger, R., Sanders, P., Schultes, D., Vetter, C.: Exact routing in large road networks using contraction hierarchies. Transport. Sci. 46(3), 388–404 (2012)CrossRef
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Sachenbacher, M., Leucker, M., Artmeier, A., Haselmayr, J.: Efficient energy-optimal routing for electric vehicles. In: Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), pp. 1402–1407 (2011)
Smart, J., Schey, S.: Battery electric vehicle driving and charging behavior observed early in the EV project. In: SAE Technical Papers, pp. 27–33 (2012)
Sweda, T.M., Dolinskaya, I.S., Klabjan, D.: Adaptive routing and recharging policies for electric vehicles. Transport. Sci. 51(4), 1326–1348 (2017)CrossRef
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Metadata
Title
A Fast Electric Vehicle Planner Using Clustering
Authors
Jaël Champagne Gareau
Éric Beaudry
Vladimir Makarenkov
Copyright Year
2021
Book
Data Analysis and Rationality in a Complex World

Print ISBN: 978-3-030-60103-4

Electronic ISBN: 978-3-030-60104-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-60104-1_3

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