An Intelligent Load Balancing Strategy for Energy Cost Minimization in EV Applications

Abstract

Energy management systems are the most significant field in the potential electric vehicles (EVs). Also, EVs are widely used because of their pollution-free nature worldwide and their specific feature of flexibility. However, the EV charging system’s significant challenges are power loss and high power consumption. Therefore, a robust load-balancing strategy is required to address the EV charging system’s issues. Therefore, a novel intelligent hybrid Buffalo-based Recurrent Energy Management System (BREMS) has been proposed to reduce the power consumption of EVs and mitigate power losses. Here, two primary functions have been done. Initially, hydro and solar sources are modeled with the help of energy storage components and a power grid to enhance the system’s efficiency. Then, the optimization fitness module is initiated to the hidden layer of the neural framework to monitor the loaded grids and distributes the loads to imbalanced grids. Implementation and designing processes have been done on the MATLAB platform. The simulation outcomes of the proposed design have been compared with conventional models in terms of power loss, cost of energy, utilization of power, and execution time. The comparative assessment shows that the presented system obtained better outcomes than other models.