An optimal method for charging station location based on big data of the SOC and immune algorithm

摘要

Estimation of the state of charge (SOC) of the battery management system (BMS) is vital for an electric vehicle (EV). Higher accuracy of SOC estimation increases the accuracy of the mileage calculation. When drivers view the remaining mileage based on SOC of the BMS, they can choose a convenient charging station to charge the EV; location of a suitable charging station is very important for an electrical vehicle. This paper introduces a dynamic optimization model based on mobile cloud and crowd-sensing data that collects the real time BMS of the EV; we present an immune algorithm for analysis and processing of these data to determine the locality of an optimal charging station. Simulation results show that this method is more efficient than the traditional model for charging stations.