Optimization of Charging Station Placement by Using Taxi Probe Data for On-Demand Electrical Bus System

Kameda, Hisashi; Mukai, Naoto

doi:10.1007/978-3-642-23854-3_64

Cited by 25 publications

(16 citation statements)

References 1 publication

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“…While a few studies use a whole country as testing area and allocate CS all over the country (Chung & Kwon, 2015), another part looks at research areas on the county level (Namdeo et al, 2014;Wirges, 2016;. But in most of the cases, cities, districts of cities or cities and the metropolitan areas are used as case study areas (Andrenacci et al, 2016;Frade et al, 2011;Kameda & Mukai, 2011;Sadeghi-Barzani et al, 2014;Wagner et al, 2014;Wood et al, 2015;Zhao, Zhang, Wei, & Zhang, 2015). Only a few studies apply the models in a small area below an administration level as they observe only a section of a district or city area (B€ ohm, 2013;Ya gCitekin, Uzuno gLu, & Karakaş, 2016).…”

Section: Case Study Regionsmentioning

confidence: 99%

A review of spatial localization methodologies for the electric vehicle charging infrastructure

Pagany

Camargo

Dorner

2018

International Journal of Sustainable Transportation

120

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With view to the high share of the transport sector in total energy consumption, e-mobility should play an important role within the transition of the energy systems. Policymakers in several countries consider electric vehicles (EV) as an alternative to fossil-fueled vehicles. In order to allow for the development of EV, the charging infrastructure has to be set up at locations with high charging potential, identified by means of various criteria such as demand density or trip length. Many methodologies for locating charging stations (CS) have been developed in the last few years. First, this paper presents a broad overview of publications in the domain of CS localization. A classification scheme is proposed regarding modeling theory and empirical application; further on, models are analyzed, distinguishing between users, route or destination centricity of the approaches and outcomes. In a second step, studies in the field of explicit spatial location planning are reviewed in more detail, that is, in terms of their target criteria and the specialization of underlying analytical processes. One divergence of these approaches lies in the varying level of spatial planning, which could be crucial depending on the planning requirements. It is striking that almost all CS locating concepts are proposed for urban areas. Other constraints, such as the lack of extensive empirical EV traffic data for a better understanding of the driving behavior, are identified. This paper provides an overview of the CS models, a classification approach especially considering the problem's spatial dimension, and derives perspectives for further research. ARTICLE HISTORY

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Section: Case Study Regionsmentioning

confidence: 99%

A review of spatial localization methodologies for the electric vehicle charging infrastructure

Pagany

Camargo

Dorner

2018

International Journal of Sustainable Transportation

120

View full text Add to dashboard Cite

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“…By employing an integer programming optimization model adapted from a Vehicle Routing Problem (VRP), Worley [20] presented an approach to perform the routing of commercial EV and distribution of charging stations simultaneously. Also based on VRP, Kameda [21] set up a model using the Node Insertion Algorithms for solving the location problem in the context of an on-demand electrical minibus system in Japan. The placement of charging stations is finally determined by means of taxi probe data.…”

Section: Literature Reviewmentioning

confidence: 99%

Electrification of a City Bus Network: An Optimization Model for Cost-Effective Placing of Charging Infrastructure and Battery Sizing of Fast Charging Electric Bus Systems

Kunith

Mendelevitch²,

Goehlich

2016

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe deployment of battery-powered electric bus systems within the public transportation sector plays an important role to increase energy efficiency and to abate emissions. Rising attention is given to bus systems using fast charging technology. This concept requires a comprehensive infrastructure to equip bus routes with charging stations. The combination of charging infrastructure and bus batteries needs a reliable energy supply to maintain a stable bus operation even under demanding conditions. An efficient layout of the charging infrastructure and an appropriate dimensioning of battery capacity are crucial to minimize the total cost of ownership and to enable an energetically feasible bus operation. In this work, the central issue of jointly optimizing the charging infrastructure and battery capacity is described by a capacitated set covering problem. A mixed-integer linear optimization model is developed to determine the minimum number and location of required charging stations for a bus network as well as the adequate battery capacity for each bus line of the network. The bus energy consumption for each route segments is determined based on individual route, bus type, traffic and other information. Different scenarios are examined in order to assess the influence of charging power, climate and changing operating conditions. The findings reveal significant differences in terms of needed infrastructure depending on the scenarios considered. Moreover, the results highlight a trade-off between battery size and charging infrastructure under different operational and infrastructure conditions. The paper addresses upcoming challenges for transport authorities during the electrification process of the bus fleets and sharpens the focus on infrastructural issues related to the fast charging concept. JEL codes: C61; L92; R42

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“…For most affordable battery EVs, such as Nissan leaf, Volkswagen Egolf, the drive range is below 100 miles per full charge [3]. Optimization of charging station locations could help extend EV drive range [11][12][13]. Different groups have been working on optimal charging station locations in big cities [12][13][14].…”

Section: ) Power Levelmentioning

confidence: 99%

EV usage and city planning of charging station installations

Yang

Gao

Farley

et al. 2015

2015 IEEE Wireless Power Transfer Conference (WPTC)

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Electric vehicles (EVs) are becoming popular in the United States. Battery EVs, powered exclusively by electricity, draw more attention because they emit zero exhaust fumes, transfer energy efficiently and can be more economical compared with internal combustion automobiles. This paper presents a comprehensive analysis of the real-world charging, driving and energy consumption patterns of electric vehicles and charging stations deployed in a college town over a period of ten months. Data collection approaches include data from charging stations and from Nissan CARWINGS telematics services. Results indicate that most charging events last fewer than three hours and most battery EVs are used locally within Athens, GA. Based on this EV usage analysis, potential impacts of wireless EV charging stations as well as a list of practical factors are discussed when implementing wireless charging stations across the city.

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Optimization of Charging Station Placement by Using Taxi Probe Data for On-Demand Electrical Bus System

Cited by 25 publications

References 1 publication

A review of spatial localization methodologies for the electric vehicle charging infrastructure

A review of spatial localization methodologies for the electric vehicle charging infrastructure

Electrification of a City Bus Network: An Optimization Model for Cost-Effective Placing of Charging Infrastructure and Battery Sizing of Fast Charging Electric Bus Systems

EV usage and city planning of charging station installations

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