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.
With the recent expansion in Self-Driving and Autonomy field, every vehicle is occupied with some kind or alter driver assist features in order to compensate driver comfort. Expansion further to fully Autonomy is extremely complicated since it requires planning safe paths in unstable and dynamic environments. Impression learning and other path learning techniques lack generalization and safety assurances. Selecting the model and avoiding obstacles are two difficult issues in the research of autonomous vehicles. Q-learning has evolved into a potent learning framework that can now acquire complicated strategies in high-dimensional contexts to the advent of deep feature representation. A deep Q-learning approach is proposed in this study by using experienced replay and contextual expertise to address these issues. A path planning strategy utilizing deep Q-learning on the network edge node is proposed to enhance the driving performance of autonomous vehicles in terms of energy consumption. When linked vehicles maintain the recommended speed, the suggested approach simulates the trajectory using a proportional-integral-derivative (PID) concept controller. Smooth trajectory and reduced jerk are ensured when employing the PID controller to monitor the terminals. The computational findings demonstrate that, in contrast to traditional techniques, the approach could investigate a path in an unknown situation with small iterations and a higher average payoff. It can also more quickly converge to an ideal strategic plan.
Electric vehicles have gained significance owing to its unavoidable supporting factors including environmental impacts and climate features. It has been noticed over last few decades that the increased number of manufacturers have focused on electric propulsion-based technology either pure electric or hybrid form with the support of electric vehicles in the automotive market. The adoption of these electric vehicle has obviously increased its competitive nature while compared to traditional internal combustion engine system. Moreover, the electric vehicles (EVs) possess substantial potential, not only in minimizing carbon emission but also in assisting required energy storage to contribute to the distributed renewable generation. There exist several increases in electric vehicle usage, but their level of massive adoption and existence by automotive consumers is connected with its delivered performance. One such important feature is the autonomous electric vehicle communication networks. This research provides a comprehensive review on overview of the electric vehicles and will discuss various existing works on autonomous driving vehicles. The paper compares existing communication networks and nuances associated in the context of an autonomous electric vehicle. Also, it critiques the existing technology and provides suggestive future work in the field to make communication networks resilient. An extensive review makes it possible to ascertain future research directions in the EV research field, which would result in massive future and instantaneous EV perception in the automotive market.
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