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AuthorShibl, M.
AuthorIsmail, L.
AuthorMassoud, Ahmed
Available date2022-03-23T06:57:27Z
Publication Date2021
Publication NameEnergies
ResourceScopus
Identifierhttp://dx.doi.org/10.3390/en14196199
URIhttp://hdl.handle.net/10576/28709
AbstractElectric vehicles (EVs) have gained in popularity over the years. The charging of a high number of EVs harms the distribution system. As a result, increased transformer overloads, power losses, and voltage fluctuations may occur. Thus, management of EVs is required to address these challenges. An EV charging management system based on machine learning (ML) is utilized to route EVs to charging stations to minimize the load variance, power losses, voltage fluctuations, and charging cost whilst considering conventional charging, fast charging, and vehicle-to-grid (V2G) technologies. A number of ML algorithms are contrasted in terms of their performances in optimization since ML has the ability to create accurate future decisions based on historical data, which are Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Long Short-Term Memory (LSTM) and Deep Neural Networks (DNN). The results verify the reliability of the use of LSTM for the management of EVs to ensure high accuracy. The LSTM model successfully minimizes power losses and voltage fluctuations and achieves peak shaving by flattening the load curve. Furthermore, the charging cost is minimized. Additionally, the efficiency of the management system proved to be robust against the uncertainty of the load data that is used as an input to the ML system.
SponsorQatar Foundation;�Qatar National Research Fund
Languageen
PublisherMDPI
SubjectBrain
Charging (batteries)
Decision trees
Deep neural networks
Electric vehicles
Information management
Nearest neighbor search
Random forests
Support vector machines
Vehicle-to-grid
Distribution grid
Distribution grid optimiza-tion
Electric vehicle charging
K-near neighbor
Nearest-neighbour
Powerloss
Random forests
Support vectors machine
Vehicle to grids
Voltage fluctuations
Long short-term memory
TitleElectric vehicles charging management using machine learning considering fast charging and vehicle-to-grid operation
TypeArticle
Issue Number19
Volume Number14
dc.accessType Open Access


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