Identification of Electric Vehicle Charging Stations Using an Optimum Energy Consumption Approach

Abstract

An Electric Vehicle (EV) is a proven solution by car manufacturers to steadily replace the conventional vehicle with a more environmentally friendly option that will reduce our dependence on nonrenewable energies. One drawback is that an EV may take many hours to reach a full charge. Reducing the charging times for EVs are one of the leading challenges for promoting this type of vehicle. Also, the introduction of EVs into the power grid increases flow in the distribution network and as a result increases power flow congestion. Traffic conditions also play a key role in affecting EV battery consumption. Disturbed traffic conditions will result in increased energy consumption of the EV battery and negatively affect the overall performance of EVs. In this research, the problem of scheduling EV battery charging and the assignment of EVs to a Charging Station (CS) is formulated as an optimization problem and will be solved using a simulated annealing optimization method. The assignment of EVs to CSs will satisfy predetermined constraints related to CSs restrictions, the EV conditions, traffic conditions, etc. The proposed approach will be demonstrated using two different scenarios of the system, one where the EVs have homogeneous components and the second, where the EVs have heterogeneous components. From the results, it will be proven that the optimal assignment of an EV occurs when the State of Charge (SoC) of the EV battery remains at its highest possible percentage when arriving at the CS. Keeping the battery SoC at a high percentage results in reduced energy consumption and less charging time.