Scenario generation for electric vehicles' uncertain behavior in a smart city environment

Soares, João; Borges, Nuno; Ghazvini, Mohammad Ali Fotouhi; Vale, Zita; Oliveira, P. B. de Moura

doi:10.1016/j.energy.2016.06.011

Cited by 40 publications

(9 citation statements)

References 37 publications

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“…Analysis of charging behavior using simulations, such as in [16], [17] contains assumptions that might not hold true in real-world scenarios. Similarly, estimation of EV behavior derived from ICE vehicle driving data [18], [19], [20] and synthetically generated data [21], [22] may not reflect the unpredictable charging behavior in everyday scenario. Other strategies such as multi-location charging, whereby employees are encouraged to charge at home as well as the workplace, to control the load have shown promising results [23].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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As the smart city applications are moving from conceptual models to development phase, smart transportation is one of smart cities applications and it is gaining ground nowadays. Electric Vehicles (EVs) are considered one of the major pillars of smart transportation applications. EVs are ever growing in popularity due to their potential contribution in reducing dependency on fossil fuels and greenhouse gas emissions. However, large-scale deployment of EV charging stations poses multiple challenges to the power grid and public infrastructure. To overcome the issue of prolonged charging time, the simple solution of deploying more charging stations to increase charging capacity does not work due to the strain on power grids and physical space limitations. Therefore, researchers have focused on developing smart scheduling algorithms to manage the demand for public charging using modeling and optimization. More recently, there has been a growing interest in data-driven approaches in modeling EV charging. Consequently, researchers are looking to identify consumer charging behavior pattern that can provide insights and predictive analytics capability. The purpose of this paper is to provide a comprehensive review for the use of supervised and unsupervised Machine Learning as well as Deep Neural Networks for charging behavior analysis and prediction. Recommendations and future research directions are also discussed.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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“…In [8], the authors propose a model to help EV drivers locate the nearest charging station, they implement an interactive application developed via SQL and PHP platforms to assign EVs their charging locations. In order to make a vision of the smart city via dispersed detection devices, the Internet of Things (IoT), the authors in [9] discusses a data base, big data, based on the history of demand for energy, the location of EVs, and the time of connection. A reservation application based on the parameters set by the EV driver to calculate the optimal path to its destination is implemented in [10].…”

Section: Introductionmentioning

confidence: 99%

Application Design Aiming to Minimize Drivers’ Trip Duration through Intermediate Charging at Public Station Deployed in Smart Cities

El-Fedany

Kiouach

Alaoui

2019

WEVJ

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Today, smart cities are turning to electric transport, carpooling and zero emission zones. The growing number of electric vehicles on the roads makes it increasingly necessary to have a public charging infrastructure. On the other hand, the main limitations of electric vehicles are the limited range of their batteries and their relatively long charging times. To avoid having problems to recharge, electric vehicle drivers must plan their journeys more thoroughly than others. At the goal of optimizing trip time, drivers need to automate their travel plans based on a smart charging solution, which will require the development of new Vehicle-to-Grid applications that will allow at the charging stations to dynamically interact with the vehicles. In this paper, we propose an architecture based on an algorithm allowing the management of charging plans for electric vehicles traveling on the road to their destination, in order to minimize the duration of the drivers’ journey including waiting and charging times. The decision taken by the algorithm based on the exploration of the data of each public supply station according to its location, number of vehicles in the queue, number of charging sockets, and rates of service.

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“…As reported in [17], the smart city is intended to deal with or mitigate, through the highest efficiency and resource optimization, the problems generated by rapid urbanization and population growth, such as energy supply, waste management, and mobility. Consequently, not only energy issues are fundamental, but also waste management and mobility aspects have to be taken into account [18,19].…”

Section: Introductionmentioning

confidence: 99%

The Smart City Energy infrastructures at the Savona Campus of the University of Genoa

Bracco¹,

Delfino

Laiolo

et al. 2016

2016 AEIT International Annual Conference (AEIT)

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This paper presents ongoing research activities and technology upgrades carried out by the Power System Research team of the University of Genoa on the Smart City test-bed facilities installed at the Savona Campus. These facilities consist of a Smart Polygeneration Microgrid (SPM) feeding the Campus, of a Smart Energy Building (SEB) connected to the SPM and acting as a "prosumer" and of an Energy Management System (EMS) controlling the Campus generating units and thermal and electrical loads. The SPM, initially set up as a grid-tied system, is now subjected to further improvements in order to be operated in islanded mode. The paper shows that all the aforementioned infrastructures constitute a real example of how to build a sustainable smart city.

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Scenario generation for electric vehicles' uncertain behavior in a smart city environment

Cited by 40 publications

References 37 publications

Machine Learning Approaches for EV Charging Behavior: A Review

Machine Learning Approaches for EV Charging Behavior: A Review

Application Design Aiming to Minimize Drivers’ Trip Duration through Intermediate Charging at Public Station Deployed in Smart Cities

The Smart City Energy infrastructures at the Savona Campus of the University of Genoa

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