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Optimal Placement of Battery Electric Bus Charging Stations Considering Energy Storage Technology: Queuing Modeling Approach

Authors: Mohsen Momenitabar; Zhila Dehdari Ebrahimi; Kelly Bengtson;

Optimal Placement of Battery Electric Bus Charging Stations Considering Energy Storage Technology: Queuing Modeling Approach

Abstract

In recent years, there has been growing attention on the electrification of the public transit network. Battery electric buses (BEBs) are among the promising alternatives to replace diesel-powered buses. However, the possible driving range from a full charge has proved a matter of concern, as has the waiting times of BEBs returning to terminal stops after completing their journeys. This study aimed to design an efficient electric transit network considering waiting times at terminal stops and two configurations of charger to avoid BEBs running out of charge: a fast charger with energy storage (ES) technology and one without. A queuing-based mathematical model was proposed. To validate the proposed model, we tested it on two sizes of network: the Mumford0 (small) and the Mumford2 (large). By conducting a sensitivity analysis, certain model parameters, including the power of fast chargers, duration of service interval, BEB energy consumption, and maximum allowable waiting time were found to have substantial impacts on the electric public transit network. ES chargers were found to have the potential to save 15.35% of total costs. Other analyses confirmed that altering the capacity of fast- and ES chargers could affect the number of chargers required in the transit network and the total cost. Policies are suggested for transit agencies to plan to optimize their electric transit networks.

Country
Australia
Keywords

mode - bus, public electric transit network, infrastructure - maintainance, 620, technology - alternative fuels, fast and ES chargers, battery-electric buses, charging stations, infrastructure - vehicle, queuing model

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    6
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Top 10%