Jingping Nie presented our paper on “A Data-Driven and Human-Centric EV Charging Recommendation System at City-Scale” at ACM e-Energy ’23, part of ACM FCRC in Orlando, FL.
In this paper, we present a practical, data-driven, and human-centric EV charging recommendation system at the city-scale based on deep reinforcement learning (DRL). The system co-optimizes the welfare of both the EV drivers and the grid. We augmented and aggregated data from various sources, including public data, location-based data companies, and government authorities, with different formats and time granularities. The data includes EV charger information, grid capacity, EV driving behavior information, and city-scale mobility. We created a 30-day per-minute unified EV charger information dataset with charging prices and grid capacity, as well as an EV driving behavior dataset with location and State of Charge (SoC) information. Our evaluation of the recommendation system shows that it is able to provide recommendations that reduce the average driver-to-charger distance and minimize the number of times chargers switch to a different driver.
We constructed an EV driving-charging-dataset for a total number of 61, 392 valid trips in Nov. 2022 and open-sourced the dataset to facilitate the research in this area: https://github.com/Columbia-ICSL/Data-Driven-Human-Centric-EV-Charging. Congratulations, Jingping!