%0 Journal Article %1 raghu_nagashree_2023_7929676 %A Nagashree, Raghu %A Gowda, Kishore %D 2023 %J GSC Advanced Research and Reviews %K (RNNs) Networks Neural Recursive %N 1 %P 076–080 %R 10.30574/gscarr.2023.15.1.0116 %T Electric vehicle charging state predictions through hybrid deep learning: A review %U https://gsconlinepress.com/journals/gscarr/content/electric-vehicle-charging-state-predictions-through-hybrid-deep-learning-review %V 15 %X 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.

@article{raghu_nagashree_2023_7929676, 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.}, added-at = {2023-07-14T12:41:35.000+0200}, author = {Nagashree, Raghu and Gowda, Kishore}, biburl = {https://www.bibsonomy.org/bibtex/2b6c6c5d10ad1187e40613c18e74b9343/gscarrjournal}, doi = {10.30574/gscarr.2023.15.1.0116}, interhash = {f24238146d5ed0f16e35894436c3e13d}, intrahash = {b6c6c5d10ad1187e40613c18e74b9343}, issn = {2581-3250}, journal = {GSC Advanced Research and Reviews}, keywords = {(RNNs) Networks Neural Recursive}, month = apr, number = 1, pages = {076–080}, timestamp = {2023-07-14T12:41:35.000+0200}, title = {Electric vehicle charging state predictions through hybrid deep learning: A review}, url = {https://gsconlinepress.com/journals/gscarr/content/electric-vehicle-charging-state-predictions-through-hybrid-deep-learning-review}, volume = 15, year = 2023 }