Hybrid Deep Learning Mechanism for Charging Control and Management of Electric Vehicles

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  •   Ashwin Kavasseri Venkitaraman

  •   Venkata Satya Rahul Kosuru

Abstract

In perspective of their environmental friendliness and energy efficiency, Electric Vehicles (EVs) are posing a threat to traditional gasoline automobiles. Identifying the future charging needs of EV users may be aided by the forecasting of states linked to EV charging. It might deliver customized charge capacity statistics based on users' real-time locations as well as direct the operation and management of charging infrastructure. Consequently, an emergent problem is the effective model of EV charging state predictions. In this study, a hybrid deep learning approach is suggested to assure safe and dependable charging operations that prevent the battery from being overcharged or discharged. A Recursive Neural Networks (RNNs) for feature extraction process is suggested to acquire adequate feature information on the battery. The bidirectional gated recurrent unit framework (GRU) was then established by the study to predict the state of the EV. The GRU receives its input from the RNNs' output, which substantially enhances the effectiveness of the model. Because of its much simpler structure, the RNN-GRU has a lower computational performance. The experimental findings demonstrate the GRU method's ability to accurately track mileage of the electric vehicle. A hybrid deep learning-based prediction approach could give quick convergence speed less error rate in comparison to the appropriate method for obtaining state of charge estimate over conventional models, as demonstrated by the extensive real-world tests.

Keywords: Charge Control, Electric Vehicle, Gated Recurrent Units (GRU), Hybrid Deep Learning (HDL), Recursive Neural Network (RNN).

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How to Cite
[1]
Venkitaraman, A.K. and Kosuru, V.S.R. 2023. Hybrid Deep Learning Mechanism for Charging Control and Management of Electric Vehicles. European Journal of Electrical Engineering and Computer Science. 7, 1 (Jan. 2023), 38–46. DOI:https://doi.org/10.24018/ejece.2023.7.1.485.