2021/11/30 by Yize Chen, Chen, Yize, Baosen Zhang +1
Engineering · #Advanced Battery Technologies Research #Advancements in Battery Materials #Electric Vehicles and Infrastructure #FOS: Electrical engineering #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2111.15147
openalex publication_date 2021/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recent proliferation in electric vehicles (EVs) are posing profound impacts over the operation of electrical grids. In particular, due to the physical constraints on charging stations' capacity and uncertainty in charging demand, it becomes an emerging challenge to design high performance scheduling algorithms to better serve charging sessions. In this paper, we design a predictive charging controller by actively incorporating each EV's state-of-charge (SOC) information, which has strong effects on the utilization of dispatchable power during peak hours. Simulation results on both synthetic and real-world EV session and charging demand data demonstrate the proposed algorithm's benefits on maximizing charging throughput and achieving higher rate of feasible charging sessions while satisfying battery and station physical constraints at the same time.