2023/07/17 by Saif Ahmad, Ahmad, Saif, Jochem Baltussen +7
Engineering · #Advanced Battery Technologies Research #Electric Vehicles and Infrastructure #FOS: Electrical engineering #Smart Grid Energy Management #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2307.08311
openalex publication_date 2023/07/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, the problem of electric vehicle (EV) charging at the workplace is addressed via a two-layer predictive algorithm. We consider a time of use (TOU) pricing model for energy drawn from the grid and try to minimize the charging cost incurred by the EV charging station (EVCS) operator via an economic layer based on dynamic programming (DP) approach. An adaptive prediction algorithm based on a non-parametric stochastic model computes the projected EV load demand over the day which helps in the selection of optimal loading policy for the EVs in the economic layer. The second layer is a scheduling algorithm designed to share the allocated power limit (obtained from economic layer) among the charging EVs during each charge cycle. The modeling and validation is performed using ACN data-set from Caltech. Comparison of the proposed scheme with a conventional DP algorithm illustrates its effectiveness in terms of supplying the requested energy despite lacking user input for departure time.