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Optimal placement of charging stations for electric vehicles in large-scale transportation networks
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
Malmö Högskola, SWE.
2019 (English)In: Procedia Computer Science / [ed] Shakshuki, E; Yasar, A; Malik, H, Elsevier B.V. , 2019, Vol. 160, p. 77-84Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V.

Place, publisher, year, edition, pages
Elsevier B.V. , 2019. Vol. 160, p. 77-84
Series
Procedia Computer Science, E-ISSN 1877-0509
Keywords [en]
Charging stations, Clectric vehicle, Optimal placement, Route node coverage, Transportation network, Charging (batteries), Data communication systems, Efficiency, Electric vehicles, Fleet operations, Health care, Internet protocols, Iterative methods, Location, Alternative fuel vehicles, Charging station, Iterative approximations, Large-scale transportation, Node coverage, Optimal placements, Optimization techniques, Transportation routes
National Category
Transport Systems and Logistics Energy Systems
Identifiers
URN: urn:nbn:se:bth-19238DOI: 10.1016/j.procs.2019.09.446ISI: 000515510100010Scopus ID: 2-s2.0-85079099240OAI: oai:DiVA.org:bth-19238DiVA, id: diva2:1394942
Conference
10th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2019 and 9th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2019, Affiliated Workshops; Coimbra; Portugal; 4 November
Note

open access

Available from: 2020-02-20 Created: 2020-02-20 Last updated: 2023-04-03Bibliographically approved
In thesis
1. On the use of traffic flows for improved transportation systems: Mathematical modeling and applications
Open this publication in new window or tab >>On the use of traffic flows for improved transportation systems: Mathematical modeling and applications
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis concerns the mathematical modeling of transportation systems for improved decision support and analysis of transportation-related problems. The main purpose of this thesis is to develop and evaluate models and methods that exploit link flows. Link flows are straightforward to obtain by measurements or estimation methods and are commonly used to describe the traffic state. The models and methods used in this thesis apply mathematical optimization techniques, computer simulations, and probabilistic methods to gain insights into the transportation network under study and provide benefits for both traffic managers and road users. 

First, we present an optimization model for allocating charging stations in a transportation network to serve owners of electric vehicles. The model utilizes a probabilistic route selection process to detect locations through which vehicles may pass. It also considers the limited driving range of electric vehicles. The iterative solution procedure finds the minimal number of minimal charging stations and their locations, which provides a lower bound of charging stations to cover each of the considered routes. Second, we present a case study, in which we argue that stationary and mobile measurement devices possess complementary characteristics. In that study, we investigate how speed cameras and probe vehicles can be used in conjunction with each other for the collection of detailed traffic data. The results show that the share of successfully observed and identified vehicles can be significantly improved by using both stationary and mobile measurement devices. Third, we present a simulation model with the intent of finding the most probable underlying routes based on hourly link flows. The model utilizes Dijkstra's algorithm to find the shortest paths and uses a straightforward statistical test procedure to find the most significant routes in the network based on replicated movements of trucks. Finally, we investigate the possibility to study how the traffic flow in one location reflects the flows in the surrounding area. The statistical basis of the proposed model is built upon measured link flows to study the dispersion of aggregate traffic flows in nodes. By considering the alternative ways vehicles can travel between locations, the model is able to determine the expected link flow that originates from a node in a nearby region.

The results of the thesis show that the link flows, which are basic descriptors of the road segments in a transportation network, can be used to study a broad range of problems in transportation.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2021. p. 101
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 8
Keywords
Mathematical modeling, Transportation systems, Link flows
National Category
Transport Systems and Logistics
Research subject
Mathematics and applications
Identifiers
urn:nbn:se:bth-22111 (URN)978-91-7295-429-8 (ISBN)
Presentation
2021-10-12, C413A/Zoom, Valhallavägen 1, 10:00 (English)
Opponent
Supervisors
Available from: 2021-09-08 Created: 2021-09-07 Last updated: 2021-11-04Bibliographically approved

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Optimal placement of Charging Stations for Electric Vehicles inlarge-scale Transportation Networks(1285 kB)231 downloads
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Fredriksson, HenrikDahl, Mattias

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