Intelligent Transportation Systems Enabled ICT Framework for Electric Vehicle Charging in Smart City

Cao, Yue; Ahmad, Naveed; Kaiwartya, Omprakash; Putrus, Ghanim; Khalid, Muhammad

doi:10.1007/978-3-319-97271-8_12

Cited by 22 publications

(11 citation statements)

References 19 publications

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“…Innovative and sophisticated approaches/tools are also needed to process the Big Data generated from the stakeholders [186]. For future connected vehicles, Vehicle-to-Infrastructure (so-called V2I) communication is required to facilitate the EVs for accessing the Internet via innovative technologies in mobile communications, such as Bluetooth, WiFi, 4G, and even 5G networks [187].…”

Section: Information and Communication Technologymentioning

confidence: 99%

A comprehensive review on electric vehicles smart charging: Solutions, strategies, technologies, and challenges

Sadeghian

Oshnoei

Mohammadi‐Ivatloo

et al. 2022

Journal of Energy Storage

141

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Section: Information and Communication Technologymentioning

confidence: 99%

A comprehensive review on electric vehicles smart charging: Solutions, strategies, technologies, and challenges

Sadeghian

Oshnoei

Mohammadi‐Ivatloo

et al. 2022

Journal of Energy Storage

141

View full text Add to dashboard Cite

“…Techniques devised in the following literature [30][31][32][33] effectively addressed the problem of selecting the CS based on closest distance or minimal waiting time. The CS-selection model devised in the literature [30][31][32][33] adapted the pricing method to reduce the congestion and increase the profit by fixing a price based on the number of EVs getting charged at every point. 33 In [34], the authors devised a method for charging EVs, where the traffic conditions and geographic location were integrated.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The CS-selection model devised in the literature [30][31][32][33] adapted the pricing method to reduce the congestion and increase the profit by fixing a price based on the number of EVs getting charged at every point. 33 In [34], the authors devised a method for charging EVs, where the traffic conditions and geographic location were integrated. The optimal path for charging was devised in the literature 35 and combined the power grid, EVs, and transportation network to offer users navigation for charging.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent optimization for charging scheduling of electric vehicle using exponential Harris Hawks technique

Devendiran¹,

Kasinathan²,

Ramachandaramurthy

et al. 2021

Int J Intell Syst

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The coordination of modern transportation system depends heavily on intelligent techniques, information assortment, and its analysis. Sensors play a crucial role in information assortment in charging scheduling of electric vehicles (EVs). EVs are destined to become inevitable due to their innate economic contribution, climate improvement, and social attributes as per United Nation's sustainable development goals. Innovation in EV has gained the interest of many researchers since it is one of the novel green transportation sectors. Moreover, EVs are essential to preserve conventional fuels and to maximize the utilization of renewable sources. Nevertheless, EVs have short driving ranges due to their battery limitation, which hinders the reliability. The charging stations (CS) for EVs are also unevenly distributed. This paper presents a novel strategy to schedule the charging points in EV CSs. The goal is to determine the convenient CS for EVs through Vehicular Ad-hoc Network (VANET) model. In this model, the CSs are determined and prioritized using four phases, such as driving, charge planning, charging scheduling, and

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“…In (13), the transition probability can be calculated as (14) where N ij is the number of transition times switching from one state to another. When taking advantage of MC to predict velocity, the vehicle speed and acceleration should be dispersed to describe the stochastic vehicle driving condition.…”

Section: B Microcosmic Velocity Prediction In On-board Vcumentioning

confidence: 99%

“…By 5G cellular network, data collected from route segments and vehicles could be rapidly sent to MEC servers [12], some control process could be manipulated in MEC servers instead of in VCUs, thus achieving the VEC control and realizing the VCU computation intensity. Some research in terms of the electric vehicle charging planning has been performed by the MEC methods, which shows dramatic improvement [13], [14]. The MEC based methods applied in VEC control to predict velocity profile, to the best knowledge of authors, are still quite seldom to be found in existing literatures.…”

Section: Introductionmentioning

confidence: 99%

A Vehicle-Environment Cooperative Control Based Velocity Profile Prediction Method and Case Study in Energy Management of Plug-in Hybrid Electric Vehicles

et al. 2019

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The vehicle-environment cooperative (VEC) control has shown a great potential to improve vehicle performance. Consequently, it is desirable to further investigate the incorporation of the VEC control. In this context, a novel method is proposed to predict the velocity profile; meanwhile, the potential of the proposed method is exploited to improve energy management performance of plug-in hybrid electric vehicles (PHEVs). In particular, a specific VEC control framework is first introduced based on the mobile edge computation (MEC). On this basis, a compound velocity profile prediction (CVPP) algorithm is developed, which merges the cloud server (CS), MEC servers, and on-board vehicle control unit (VCU), and provides more accurate and reasonable prediction results. Finally, a case study is conducted that applies the proposed CVPP method to energy management of PHEVs. The simulation results manifest that the performance of the proposed energy management strategy (EMS) is dramatically improved after incorporating the forecasted velocity profile information given by the proposed CVPP method. INDEX TERMS Vehicle-environment cooperative (VEC) control, mobile edge computation (MEC), velocity profile prediction, energy management, plug-in hybrid electric vehicles (PHEVs).

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Intelligent Transportation Systems Enabled ICT Framework for Electric Vehicle Charging in Smart City

Cited by 22 publications

References 19 publications

A comprehensive review on electric vehicles smart charging: Solutions, strategies, technologies, and challenges

A comprehensive review on electric vehicles smart charging: Solutions, strategies, technologies, and challenges

Intelligent optimization for charging scheduling of electric vehicle using exponential Harris Hawks technique

A Vehicle-Environment Cooperative Control Based Velocity Profile Prediction Method and Case Study in Energy Management of Plug-in Hybrid Electric Vehicles

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