Evaluation of electric vehicle power routing and charging analysis using artificial neural network synchronized with 5G-network

Abstract

To motivate the advanced National Clean Air Program (NCAP) strategy announced by the Indian Government in 2017, automizing the routing control on electric vehicle (EV) is a challenging area. According to the public authority guideline, the air contamination issues over the nation focus on accomplishing a 20% to 30% decrease in vehicular outflows in 2024 by enhancing EV mobility. However electric vehicle's limitations on battery capacity and route strategy to charging stations to preserve an adequate level of battery charge still need automized communication strategy. Fixed route solutions might not always be executable because battery consumption varies on several external factors. To better fit the real situation, this study proposes the notion of breaking down the self-adaptive online routing by encountering automatic path detection concerning battery efficiency with adaptive charging decisions. An inventory control-style charging technique is suggested to account for stochastic battery use. In the proposed work, by using an artificial neural network, the power demand and energy consumption of the EV world will be monitored and controlled by using 5G coverage on the smart city evolution. The obtained simulation analysis confirms that in comparison to current conventional and fixed tariff schemes, 5G accompanied EV accomplish an examined rate of SoC charge around 19.95 MW holding a peak range of 108.96 MW. Also, an evaluated typical power loss rate of 2.873 kWH which is less than 11.95% through frequency band analysis with signal-to-noise ratio and downlink power utilization.