Unsupervised learning for deploying smart charging public infrastructure for electric vehicles in sprawling cities

Marino, Carlos; Marufuzzaman, Mohammad

doi:10.1016/j.jclepro.2020.121926

Cited by 13 publications

(6 citation statements)

References 48 publications

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“…Although the flexibility of electric buses has decreased, up to 32% of the electricity can still be generated by solar PV due to the timing of charging. Marino and Marufuzzaman, 29 have suggested that electric cars (EVs) use public smart charging stations (CS) in a significant metropolis in Latin America. The decreased availability of home charging was a relevant contrast between established and emerging economies, as was the situation in Latin American cities.…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Electric vehicle smart charging with network expansion planning using hybrid COA‐CCG‐DLNN approach

Rahila,

Soundra Devi,

Radhika

et al. 2024

Optim Control Appl Methods

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Integrating network expansion planning into electric vehicle (EV) smart charging solutions involves designing scalable infrastructure to accommodate the growing demand for electric mobility while considering grid capacity and energy distribution efficiency. This paper proposes a hybrid approach for EV smart charging with network expansion planning. The hybrid technique is the joint execution of the coati optimization algorithm (COA) and cascade‐correlation‐growing deep learning neural network, commonly known as the COA‐CCG‐DLNN technique. The objective of the proposed method is to minimize the cost of charging EVs, and it forecasts the best course of action. EV charging with network expansion is based on vehicle‐to‐building (V2B), vehicle‐to‐grid (V2G), and grid‐to‐vehicle (G2V). The COA approach is used to minimize the cost of EV charging and the CCG‐DLNN approach is used to predict the optimal solution for the system. The proposed method is executed on the MATLAB platform and is compared with existing techniques like particle swarm optimization (PSO), heap‐based optimization (HBO), and wild horse optimization (WHO). The proposed method achieves a low cost of $1.33 and a high accuracy of 99.5% compared with other existing techniques. The performance metrics for the proposed method include 2996.348 as the best result, 3000.100 as the mean, 3001.261 as the worst, and a standard deviation of 1.160348, along with a median of 2998.816, all of which outperform other existing methods.

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Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Electric vehicle smart charging with network expansion planning using hybrid COA‐CCG‐DLNN approach

Rahila,

Soundra Devi,

Radhika

et al. 2024

Optim Control Appl Methods

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“…The experience of other countries, such as Norway and France, with the implementation of electromobility, understood as a high rate of registered electric vehicles, is described by Holden, Banister, Gilpin, Gössling, and Linnerud (2020). The topic of emerging economies, where electromobility is constantly developing, is discussed by Xue and Gwee (2017) and Marino and Marufuzzaman (2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of the Development of Electromobility Strategies in Selected Cities in Poland

Pokorska

2023

ERSJ

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Purpose:The aim of the article is to assess the level of development of electromobility strategies in selected cities in Poland based on the results obtained from the expert method. Design/Methodology/Approach: The article uses the method of multidimensional comparative analysis, which, based on the results of expert method research, allowed for the assessment of cities in Poland in terms of the development of electromobility strategies, understood in the work as the number of low-emission cars, i.e. electric and hybrid. Findings: Despite the turbulence in the international environment, the electric car market is constantly developing. The article contains ranking information regarding the predispositions of cities to achieve sustainable mobility in terms of implementing electromobility strategies, along with descriptions indicating the determinants of a given position. Practical Implications: The analysis carried out can provide cities and their local authorities with a ready-made solution supporting the development of electromobility strategies in the form of a key set of indicators for sustainable urban mobility along with the desired direction of their development. In addition, the article contains ranking information on the management of sustainable urban mobility in the aspect of implementing electromobility strategies in the largest Polish cities, along with an analysis of the reasons for taking a specific place in the ranking, which may influence the change of electromobility strategies in these cities. Originality/Value: Taking into account the current stage of development of electromobility in Poland, attention should be paid to the lack of comprehensive analyzes of the impact of the phenomenon in question on this area.

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“…An optimal planning of plug-in EV fast charging stations using an auction-based method was proposed [10]. A unsupervised learning methodology for deploying charging public infrastructure for EVs in sprawling cities was proposed [11]. Fast-charging station siting and sizing based on a mixedinteger nonlinear programming approach was proposed [12].…”

Section: Introductionmentioning

confidence: 99%

Optimal siting and sizing of electric taxi charging stations considering transportation and power system requirements

Clairand

González²,

Kumar³

et al. 2022

Energy

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Unsupervised learning for deploying smart charging public infrastructure for electric vehicles in sprawling cities

Cited by 13 publications

References 48 publications

Electric vehicle smart charging with network expansion planning using hybrid COA‐CCG‐DLNN approach

Electric vehicle smart charging with network expansion planning using hybrid COA‐CCG‐DLNN approach

Comparative Analysis of the Development of Electromobility Strategies in Selected Cities in Poland

Optimal siting and sizing of electric taxi charging stations considering transportation and power system requirements

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