  In ridepooling systems with electric fleets, charging is a complex
decision-making process. Most electric vehicle (EV) taxi services require
drivers to make egoistic decisions, leading to decentralized ad-hoc charging
strategies. The current state of the mobility system is often lacking or not
shared between vehicles, making it impossible to make a system-optimal
decision. Most existing approaches do not combine time, location and duration
into a comprehensive control algorithm or are unsuitable for real-time
operation. We therefore present a real-time predictive charging method for
ridepooling services with a single operator, called Idle Time Exploitation
(ITX), which predicts the periods where vehicles are idle and exploits these
periods to harvest energy. It relies on Graph Convolutional Networks and a
linear assignment algorithm to devise an optimal pairing of vehicles and
charging stations, in pursuance of maximizing the exploited idle time. We
evaluated our approach through extensive simulation studies on real-world
datasets from New York City. The results demonstrate that ITX outperforms all
baseline methods by at least 5% (equivalent to $70,000 for a 6,000 vehicle
operation) per week in terms of a monetary reward function which was modeled to
replicate the profitability of a real-world ridepooling system. Moreover, ITX
can reduce delays by at least 4.68% in comparison with baseline methods and
generally increase passenger comfort by facilitating a better spread of
customers across the fleet. Our results also demonstrate that ITX enables
vehicles to harvest energy during the day, stabilizing battery levels and
increasing resilience to unexpected surges in demand. Lastly, compared to the
best-performing baseline strategy, peak loads are reduced by 17.39% which
benefits grid operators and paves the way for more sustainable use of the
electrical grid.
