Prediction of electric vehicle charging-power demand in realistic urban traffic networks

Arias, Mariz B.; Kim, Myungchin; Bae, Sungwoo

doi:10.1016/j.apenergy.2017.02.021

Cited by 153 publications

(58 citation statements)

References 38 publications

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“…For any such scheme, the challenge of predicting short-term EV demand profiles is key to success. Markovchain traffic models are proposed to achieve this in [113] using real-time CCTV data. Using public vehicle fleets with pre-specified schedules (such as buses [114] and waste disposal fleets) could greatly simplify the problem of predictable coordination.…”

Section: Electrification Of the Sectormentioning

confidence: 99%

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

et al. 2019

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Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.

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Section: Electrification Of the Sectormentioning

confidence: 99%

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

et al. 2019

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“…Alizadeh et al [40] proposed a stochastic model based on queue theory for BEV and plug-in hybrid electric vehicle charging demand. Arias et al [41] presented a time-spatial EV charging-power demand forecast model at fast-charging stations located in urban areas. Based on the data of electric vehicles in China, Cai et al [42] analyzed the impact of large-scale access of electric vehicles on the grid and the environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles

2020

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Due to the limited power cell performance of battery electric vehicles (BEVs), BEV drivers endure a short cruising range and a long charging time. Additionally, uneven charging facilities and unreasonable charging arrangements result in partial queuing and partial idling of charging stations. To solve these problems, it is critical to understand BEV charging behavior and its influential factors. Considering the urgency of BEV charging, BEV drivers tend to choose fast charging when BEV is in driving state. This study investigates fast charging behavior by utilizing private BEV connected data collected from Beijing. First, 130 private BEVs with travel rules were screened out. Using seven months of BEV data, a total of 15,752 trajectories were identified, among which 2161 have fast charging behavior. According to the relationship between fast charging behavior and some influential factors, including battery modeling, driving behavior, weather and environment, and even user habit, were empirically investigated. Moreover, the battery state of charge at the start time, time-origin, travel time duration, driving distance, driving speed, wind power, temperature, and last-fast-status are determined as significant influencing factors. Lastly, a prediction model based on the significant factors is proposed to estimate whether there is fast charging in a day trajectory. The proposed model achieves the best accuracy over compared models, i.e., univariate linear regression (ULR) with several factors and multivariate linear regression (MLR) model. The study is expected to help better understand fast charging behavior and further contribute to the future improvement of fast charging efficiency.

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“…(Luo, Zhu, Wan, Zhang, & Li, 2016;Salah, Ilg, Flath, Basse, & Dinther, 2015) Studies in both rural and urban areas using power distribution of UK, which is heavily loaded already, reveal that charging PEVs is varying in density. (Neaimeh et al, 2015) Studies in South Korea (Arias & Bae, 2016;Arias, Kim, & Bae, 2017), forecast PEVs' electrical demand using weather conditions and real-world traffic distribution data of different periods such as: weekdays, weekends, summers and winters; in commercial and residential sites. For each different period, different charging demand was revealed.…”

Section: Introductionmentioning

confidence: 99%

Electrical Vehicles Charging Coordination by Fuzzy Logical System

Kiliçarslan¹,

Ateş²,

Aydilek³

et al. 2018

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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Today, with the rapid technological development, interest in electric vehicles is also increasing. This raises the question of what the effects of the vehicles on the electric power network will be. In this article, the adverse effects of charging scenarios of electric vehicles' batteries on the electric power network are examined and fuzzy logic based solutions are proposed to prevent or reduce the effects of charging electric vehicles during peak hours. In this article a fuzzy logic system that is providing the cheapest charging prices, also reducing the impact on the load curve of Turkey's electrical network is provided.

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Prediction of electric vehicle charging-power demand in realistic urban traffic networks

Cited by 153 publications

References 38 publications

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles

Electrical Vehicles Charging Coordination by Fuzzy Logical System

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