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In ridepooling systems with electric fleets, charging is a complexdecision-making process. Most electric vehicle (EV) taxi services requiredrivers to make egoistic decisions, leading to decentralized ad-hoc chargingstrategies. The current state of the mobility system is often lacking or notshared between vehicles, making it impossible to make a system-optimaldecision. Most existing approaches do not combine time, location and durationinto a comprehensive control algorithm or are unsuitable for real-timeoperation. We therefore present a real-time predictive charging method forridepooling services with a single operator, called Idle Time Exploitation(ITX), which predicts the periods where vehicles are idle and exploits theseperiods to harvest energy. It relies on Graph Convolutional Networks and alinear assignment algorithm to devise an optimal pairing of vehicles andcharging stations, in pursuance of maximizing the exploited idle time. Weevaluated our approach through extensive simulation studies on real-worlddatasets from New York City. The results demonstrate that ITX outperforms allbaseline methods by at least 5% (equivalent to $70,000 for a 6,000 vehicleoperation) per week in terms of a monetary reward function which was modeled toreplicate the profitability of a real-world ridepooling system. Moreover, ITXcan reduce delays by at least 4.68% in comparison with baseline methods andgenerally increase passenger comfort by facilitating a better spread ofcustomers across the fleet. Our results also demonstrate that ITX enablesvehicles to harvest energy during the day, stabilizing battery levels andincreasing resilience to unexpected surges in demand. Lastly, compared to thebest-performing baseline strategy, peak loads are reduced by 17.39% whichbenefits grid operators and paves the way for more sustainable use of theelectrical grid.

Improving Operational Efficiency In EV Ridepooling Fleets By Predictive Exploitation of Idle Times