Improved deep learning model for accurate energy demand prediction and conservation in electric vehicles integrated with cognitive radio networks

In the smart transport system, the immense growth of electric vehicles (EVs) and their charging demand is on the rise. However, the prediction of this demand has become a major issue. An increasing electrical vehicle number will result in a decrease the greenhouse gas releases. In the EV, the battery's capacity is limited and mileage anxiety is tedious. For the energy conservation of electric vehicles, many studies have been applied based on this concept. The problems addressed in existing research work are high in energy conservation. To overcome this issue, this paper proposed a model of Empirical Mode Decomposition with CNN and optimized with Seagull Optimization Algorithm (EMD-CNN-SOA). This proposed work provides an accurate prediction of demand for energy conservation and it reduces the burden on electric grids while minimizing the cost of charging. Cognitive radio (CR) in the form of wireless communication will revolutionize transportation through intelligent-based smart technology and it will anticipate the user needs in the aspects of detection of available bandwidths and frequencies then seamlessly connect the infrastructure and consumer devices. It will improve the safety of mobility and adapt to the current environmental situation, informing the driver about traffic congestion which saves energy. Cognitive radio sensors in the transportation will alert and measure the on-site real time conditions. The accuracy rate for the energy conservation in electric vehicles of TWC, LSTM 66.13%, Deep CNN 78.91%, RNN 83.46%, and proposed work of EMD-CNN-SOA 88.23%. Similarly, for CRN the accuracy rate of LSTM is 69.16%, Deep CNN is 86.25%, RNN is 84.37%, and the proposed work of EMD-CNN-SOA is 92.59%.