Intelligent Parking Garage EV Charging Scheduling Considering Battery Charging Characteristic

• Published in: IEEE transactions on industrial electronics (1982) Vol. 65; no. 3; pp. 2806 - 2816
• Main Authors: Wei, Zhe, Li, Yue, Zhang, Yongmin, Cai, Lin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Admission control; Batteries; Battery chargers; Battery charging characteristic; Computer simulation; electric vehicle (EV) charging; Electric vehicle charging; Electric vehicles; hierarchical control; Job shop scheduling; Parking; Performance degradation; Pricing; Production scheduling; Scheduling; State of charge; Time of use
• DOI: 10.1109/TIE.2017.2740834
• ISSN: 0278-0046

This paper studies the electric vehicle (EV) charging scheduling problem under a parking garage scenario, aiming to promote the total utility for the charging operator subject to the time-of-use (TOU) pricing. Different from most existing works, we develop a multicharging system incorporating the practical battery charging characteristic, and design an intelligent charging management mechanism to maximize the interests of both the customers and the charging operator. First, to ensure the quality of service for each client, we implement an admission control mechanism to guarantee all admitted EVs' charging requirements being satisfied before their departure. Second, we formulate the charging scheduling process as a deadline constrained causal scheduling problem. Then, we propose an adaptive utility oriented scheduling (AUS) algorithm to optimize the total utility for the charging operator, which can robustly achieve low task declining probability and high profit. The charging operator can also apply the discussed reservation mechanism to mitigate the performance degradation caused by the charging information mismatching with vehicle stochastic arrivals. Finally, we conduct extensive simulations based on realistic EV charging parameters and TOU pricing. Simulation results exhibit the effectiveness of the proposed AUS algorithm in achieving desirable performance compared with other benchmark scheduling schemes.