Cooperative Learning for Smart Charging of Shared Autonomous Vehicle Fleets

We study the operational problem of shared autonomous electric vehicles that cooperate in providing on-demand mobility services while maximizing fleet profit and service quality. Therefore, we model the fleet operator and vehicles as interactive agents enriched with advanced decision-making aids. Our focus is on learning smart charging policies (when and where to charge vehicles) in anticipation of uncertain future demands to accommodate long charging times, restricted charging infrastructure, and time-varying electricity prices. We propose a distributed approach and formulate the problem as a semiMarkov decision process to capture its stochastic and dynamic nature. We use cooperative multiagent reinforcement learning with reshaped reward functions. The effectiveness and scalability of the proposed model are upgraded through deep learning. A mean-field approximation deals with environment instabilities, and hierarchical learning distinguishes high-level and low-level decisions. We evaluate our model using various numerical examples based on real data from ShareNow in Berlin, Germany. We show that the policies learned using our decentralized and dynamic approach outperform central static charging strategies. Finally, we conduct a sensitivity analysis for different fleet characteristics to demonstrate the proposed model's robustness and provide managerial insights into the impacts of strategic decisions on fleet performance and derived charging policies.

Automated summary

This study focuses on the operational problem of shared autonomous electric vehicles, aiming to maximize fleet profit and service quality through advanced decision-making aids. It proposes a distributed approach and uses deep learning to enhance the effectiveness and scalability of the model. The model outperforms central static charging strategies and provides insights into the impacts of strategic decisions on fleet performance and charging policies.