Electric vehicles charging management using machine learning considering fast charging and vehicle-to-grid operation
Author | Shibl, M. |
Author | Ismail, L. |
Author | Massoud, Ahmed |
Available date | 2022-03-23T06:57:27Z |
Publication Date | 2021 |
Publication Name | Energies |
Resource | Scopus |
Identifier | http://dx.doi.org/10.3390/en14196199 |
Abstract | Electric 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. |
Sponsor | Qatar Foundation;�Qatar National Research Fund |
Language | en |
Publisher | MDPI |
Subject | Brain 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 |
Type | Article |
Issue Number | 19 |
Volume Number | 14 |
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Computer Science & Engineering [2281 items ]
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Electrical Engineering [2568 items ]