… CONCLUSION In this paper, we proposed different models to determine when to charge
an Electric Vehicle (EV) considering only the current state of the vehicle, without the need to …

… a solution for Electric Vehicles (EVs) energy management in smart cities, where a deep
learning approach is used to enhance the energy consumption of electric vehicles by trajectory …

… coordinated, and smart charging of plug-in hybrid electric vehicles (PHEVs) on the optimal
… Moreover, a deep learning gated recurrent unit technique is designed to forecast renewable …

… In this study, a deep neural network (DNN) based method is proposed to estimate SOC with
… to the transfer learning nature. Compared with developing a new DNN, transfer learning can …

… of electric vehicles (EVs). To analyze the application of deep learning in electric vehicles’
power … SOC estimation in deep learning methods. First, the specific technical processes of the …

… The energy consumption of the electric vehicle considered in this study is specified as an
integration of output power of the vehicle at the battery terminal. The equations for electric …

… This work suggests a hybrid deep learning strategy for managing and controlling electric
vehicle charging in order to address such issues. To obtain sufficient feature data on the battery…

… study of deep learning approaches to forecast the super-short-term stochastic charging load
of plug-in electric vehicles. Several popular and novel deep-learning based methods have …

… The study proposed in this paper uses Deep Learning, in particular deep Convolutional
Neural Networks (CNN), in order to develop data-based models with low computational …

… supported by deep learning models. This research provided a large dataset for electric
vehicle (EV) batteries that included cleaned data on battery charging from hundreds of vehicles. …