A Data-Driven and Human-Centric EV Charging Recommendation System at City-Scale

Abstract

Electric vehicles (EVs) have gained widespread popularity in recent years, and the scheduling and routing of EV charging have impacted the welfare of both EV drivers and the grid. 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. The dataset we prepared for training the DRL agent, including augmented EV driving data and charging station information, will be open-sourced to benefit future research in the community.