An agent based approach for the development of EV fleet Charging Strategies in Smart Cities

Mureddu, Mario; Scala, Antonio; Chessa, Alessandro; Caldarelli, Guido; Musio, Maura; Damiano, Alfonso

doi:10.1109/ievc.2014.7056178

Cited by 6 publications

(7 citation statements)

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“…BACKGROUND Recently the installation smart city is envisioned to constitute a global research endeavor [9]. Policymakers as well as R&Ds have joined the consortium that is channeling its academic and research expertise towards the deployment of transport oriented cities (TOC).…”

Section: Contributionmentioning

confidence: 99%

“…Saas constitutes the topmost layer for the cloud computing architecture where the services are delivered in broadcast mode, i.e. the consumer gains public access to the service installed in the remote cloud hardware [9]. Multiple users are allowed to have the services via APIs or web browsers at the same time only after appropriate authentication constraints are fulfilled.…”

Section: Contributionmentioning

confidence: 99%

“…The prime ideology on which the model is established is remote accessibility and remote management. Plenty of claims and proposals are found in the literatures which are working independently in diverse domains such as security in information management of smart grids [4], smart grid dynamic energy management (DEM) [8], agent based simulation of electric vehicles fleet charging strategies [9] and many more. Fig.…”

Section: Contributionmentioning

confidence: 99%

“…Above all, the utmost concern is the "RANGE ANXIETY" felt by drivers of conventional vehicles. In a nutshell, while executing a fully electrified fleet, the uncoordinated charging of xEVs population may pose serious impact on reliable and efficient operation of the associated supply system [9], [11], [16]. Use of proper charging strategies may circumvent significant proportion of burdens from the overall architecture [11].…”

mentioning

confidence: 99%

“…Moreover, the massive penetration of utilities into the cloud infrastructure triggers tsunami of data generation synonymously "Big Data" [4], [7]. Robust data analytic tools and techniques supplemented with fast processing elements are required to manage and mine such datasets, with an aim to come up with a smart decision making paradigm [6], [7], [8].1.1 MOTIVATION With the advent of rigorous research and development efforts and stringent protocols related to vehicle emissions [9], fuel economy, constraints in conventional energy reserves and so called global warming, electric vehicles specifically electric/plug-in electric and plug in hybrid electric vehicles (xEVs) are receiving an utmost attention from automobile industries, governments, R&Ds, vehicle vendors as well as consumers as they seem to serve as the sustainable and efficient power trains for the coming generations. According to survey in [10] and [11], during a short period of six years from 2008 to mid of 2014, about a quarter million plug-in hybrid electric vehicles have already been launched into US roadways.…”

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confidence: 99%

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Plug-in Electric Vehicle to Cloud Data Analytics for Charging Management

Hussain¹,

Alam²,

Beg³

2017

IJET

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Abstract-Electric, plug in electric and plug in hybrid electric vehicles (xEVs) are receiving an utmost attention from automobile industries, policymakers, R&D agencies and vehicle vendors in the contemporary smart transportation era. Penetration of electric vehicle fleet into the existing charging infrastructure multiplies the load on the underlying grid system. Smart grid technologies in collaboration with smart charging management strategies may circumvent the power load, thus enabling a reliable, efficient, consistent and flexible operation of the underlying power grid system. This work demonstrates commercially viable multi-tier cloud enabled vehicle to cloud (V2C) smart fleet charging model for coordinating the charging of xEVs fleet that can support vehicle mobility satisfying the triangle equivalence namely minimum charging tariff, shortest travelling distance and minimum waiting time at the charging station. Following a multi-tier cloud infrastructure for interactive and real-time control and monitoring of the massive vehicle fleet, this work also highlights the Big Data research thrusts, opportunities and challenges that are being evolved due to integration of a distributed cloud framework with the intelligent entities like smart meters, smart charging stations, xEVs etc. for commercial viability, implementation and deployment of emerging transport oriented cities (TOCs).Keyword-Big-Data, Range anxiety, Vehicle to Cloud, xEVs Charging Management I. INTRODUCTION The development agenda in smart cities primarily demand green technology transportation. Proactive measures are needed to nullify the gaseous emissions from vehicles run by non-conventional fuels like gasoline [1]. Due to the substantial price-hike in the crude oil and prolonged dependence on foreign oil over the past decades, power GENCOs as well as the consumer, are directly or indirectly forced to opt for alternative energy sources [1]. The hybrid cars introduced by automotive industries like Nissan, Honda and Toyota are a step towards this innovation attempting to replace the Internal Combustion Engines (ICEs) with rechargeable batteries and electric motors. The bulk of the research is going on to ensure a zero-emission mode of transport. Fortunately positive results have been shown by such inventions as Electric/Plug-in electric/ Plug-in Hybrid Electric Vehicles (xEVs) which can serve as the best recourse in this storyline. Conceptually, xEVs integrate the electrical networks with so called data and communication infrastructures through smart metering and sensing utilities [2]. The heterogeneous data generated from these devices roots the use of efficient data management techniques to deliver a consistent, reliable and real time operation of the massive vehicle fleet [3]. Cloud computing is an emerging approach, that is envisaged to provide a flexible, secure and cost effective platform for storage, as well as execution of the computing resources thereby offering a robust architecture destined to have a reliable and real time operation of e...

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Section: Contributionmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Plug-in Electric Vehicle to Cloud Data Analytics for Charging Management

Hussain¹,

Alam²,

Beg³

2017

IJET

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A Complex Network Approach for the Estimation of the Energy Demand of Electric Mobility

Mureddu

Facchini

Scala

et al. 2018

Sci Rep

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We study how renewable energy impacts regional infrastructures considering the full deployment of electric mobility at that scale. We use the Sardinia Island in Italy as a paradigmatic case study of a semi-closed system both by energy and mobility point of view. Human mobility patterns are estimated by means of census data listing the mobility dynamics of about 700,000 vehicles, the energy demand is estimated by modeling the charging behavior of electric vehicle owners. Here we show that current renewable energy production of Sardinia is able to sustain the commuter mobility even in the theoretical case of a full switch from internal combustion vehicles to electric ones. Centrality measures from network theory on the reconstructed network of commuter trips allows to identify the most important areas (hubs) involved in regional mobility. The analysis of the expected energy flows reveals long-range effects on infrastructures outside metropolitan areas and points out that the most relevant unbalances are caused by spatial segregation between production and consumption areas. Finally, results suggest the adoption of planning actions supporting the installation of renewable energy plants in areas mostly involved by the commuting mobility, avoiding spatial segregation between consumption and generation areas.

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