Multi‐objective optimization for optimal energy transporting path and energy distribution in electric vehicles energy internet

Abstract

SummaryEnergy internet permits the power to stream lithely for broadcast and it aids in transporting energy to each user using electric vehicles (EVs). The energy that EVs utilize is generated using certain sources. Still, considering poor weather, the power station averts the production of energy and needs to acquire power transmitted to another location. One resolution is to set stations that aid to save surfeit energies and minimize energy loss during transportation. This paper devises a new model for choosing charging stations to charge EVs. Energy is transferred from the energy source to the charge station via bus station through optimal path using Battle Royale Jaya Optimization (BRJO) routing with fitness attributes like distance to select the shortest path and energy. The prediction of energy is done with a deep recurrent neural network (DRNN) and the charge is distributed optimally to EVs by employing a proposed Competitive Swarm‐Battle Royale Jaya Optimization (CSBRJO) by utilizing multi‐parameters, like priority, distance, predicted energy, waiting time, and EV count needed for charging. The CSBRJO outperformed with the smallest distance of 6.935 km, energy consumption of 1.676 J, path length of 5.05 m, and waiting time of 4.645 sec.