Electric vehicle charging state predictions through hybrid deep learning: A review
Dept. of Electrical Engineering, Bharath Institute of Higher Education and Research, India.
Review Article
GSC Advanced Research and Reviews, 2023, 15(01), 076–080.
Article DOI: 10.30574/gscarr.2023.15.1.0116
Publication history:
Received on 01 March 2023; revised on 10 April 2023; accepted on 13 April 2023
Abstract:
This review paper discusses the application of hybrid deep learning techniques for predicting the charging state of electric vehicles. The paper highlights the importance of accurate predictions for the efficient management of electric vehicle charging stations. The review focuses on the use of recursive neural networks (RNNs) and the gated recurrent unit (GRU) framework in hybrid deep learning models, which have shown promising results in previous studies. In addition to hybrid deep learning, the paper also examines the use of support vector machines (SVMs) and artificial neural networks (ANNs) in charging state prediction. The strengths and weaknesses of these different approaches are analyzed and compared. The paper concludes that hybrid deep learning models, particularly those using RNNs and GRUs, are a promising approach for accurately predicting electric vehicle charging states. The paper also suggests potential areas for future research to further improve the accuracy and efficiency of charging state predictions.
Keywords:
Recursive Neural Networks (RNNs); Gated Recurrent Unit Framework (GRU); Hybrid deep learning; Support Vector Machines (SVMs); Artificial Neural Networks
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
