Sensitivity analysis on battery modeling to large-scale PHEV/PEV charging algorithms

An in-depth need exists to address the potential problems caused by the emergence of plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) within in the next 20 years. The large penetration of these vehicles into the marketplace poses a potential threat to the existing power grid. A large number of PHEVs/ PEVs may cause serious system instability without a sophisticated control strategy. Energy storage is the key enabling technology for PHEVs/PEVs. The battery state information is critical to ensure optimal utilization of the available energy. It enables optimal control over the battery's charging and discharging process, thereby reducing the risk of overcharge or undercharge and prolonging battery life. In this paper, we first simulate real-world parking deck scenarios and implement four types of battery models (i.e., the linear model, relaxation model, hysteresis model, and combined model). We then evaluate optimal performance of the proposed large-scale PHEV/PEV charging algorithms under certain operating conditions. We characterize system performance and illustrate the importance of battery modeling to large-scale charging algorithms. The simulation results provide a general overview of the impact of battery modeling on optimal performance.