Next Article in Journal
Voltage Reduction in Medium Voltage Distribution Systems Using Constant Power Factor Control of PV PCS
Previous Article in Journal
Review of Formation and Gas Characteristics in Shale Gas Reservoirs
Open AccessArticle

Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers

1
Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
2
Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar
*
Author to whom correspondence should be addressed.
Energies 2020, 13(20), 5429; https://doi.org/10.3390/en13205429
Received: 15 August 2020 / Revised: 2 October 2020 / Accepted: 13 October 2020 / Published: 17 October 2020
(This article belongs to the Section Electric Vehicles)
Coordinated charging of electric vehicles (EVs) improves the overall efficiency of the power grid as it avoids distribution system overloads, increases power quality, and decreases voltage fluctuations. Moreover, the coordinated charging supports flattening the load profile. Therefore, an effective coordination technique is crucial for the protection of the distribution grid and its components. The substantial power used through charging EVs has undeniable negative impacts on the power grid. Additionally, with the increasing use of EVs, an effective solution for the coordination of EVs charging, particularly when considering the anticipated proliferation of EV fast chargers, is imminently required. In this paper, different machine learning (ML) approaches are compared for the coordination of EVs charging. The ML models can predict the power to be used in EVs charging stations (EVCS). Due to its ability to use historical data to learn and identify patterns for making future decisions with minimal user intervention, ML has been utilized. ML models used in this paper are (1) Decision Tree (DT), (2) Random Forest (RF), (3) Support Vector Machine (SVM), (4) Naïve Bayes (NB), (5) K-Nearest Neighbors (KNN), (6) Deep Neural Networks (DNN), and (7) Long Short-Term Memory (LSTM). These approaches are chosen as they are classifiers known to have the leading results for multiclass classification problems. The results found shed insight on the importance of the techniques used and their high potential in providing a reliable solution for the coordinated charging of EVs, thus improving the performance of the power grid, and reducing power losses and voltage fluctuations. The use of ML provides a less complex method to coordinate EVs, in comparison with conventional optimization techniques such as quadratic programming, and the use of ML is faster as it requires less computational power. LSTM provided the best results with an accuracy of 95% for predicting the most appropriate power rating (PR) for EVCS, followed by RF, DT, DNN, SVM, KNN, and NB. Additionally, LSTM was also the model with the smallest error rate, at a value of ±0.7%, followed by RF, DT, KNN, SVM, DNN, and NB. The results obtained from the LSTM model were similar to the results obtained from past literature using quadratic programming, with the increased speed and simplicity of ML. View Full-Text
Keywords: coordinated electric vehicles charging; cyber-physical systems (CPSs); Decision Tree (DT); Deep Neural Network (DNN); electric vehicles charging stations (EVCS); K-Nearest Neighbors (KNN); Long Short-Term Memory (LSTM); machine learning (ML); Naïve Bayes (NB); power rating (PR); Random Forest (RF); Recurrent Neural Networks (RNN); smart grid; Support Vector Machine (SVM) coordinated electric vehicles charging; cyber-physical systems (CPSs); Decision Tree (DT); Deep Neural Network (DNN); electric vehicles charging stations (EVCS); K-Nearest Neighbors (KNN); Long Short-Term Memory (LSTM); machine learning (ML); Naïve Bayes (NB); power rating (PR); Random Forest (RF); Recurrent Neural Networks (RNN); smart grid; Support Vector Machine (SVM)
Show Figures

Figure 1

MDPI and ACS Style

Shibl, M.; Ismail, L.; Massoud, A. Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers. Energies 2020, 13, 5429.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop