Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning (Papers Track) Spotlight

Jon Donadee (LLNL); Jacob Pettit (LLNL); Ruben Glatt (LLNL); Brenden Petersen (Lawrence Livermore National Laboratory)

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Abstract

New forms of on-demand transportation such as ride-hailing and connected autonomous vehicles are proliferating, yet are a challenging use case for electric vehicles (EV). This paper explores the feasibility of using deep reinforcement learning (DRL) to optimize a driving and charging policy for a ride-hailing EV agent, with the goal of reducing costs and emissions while increasing transportation service provided. We introduce a data-driven simulation of a ride-hailing EV agent that provides transportation service and charges energy at congested charging infrastructure. We then formulate a test case for the sequential driving and charging decision making problem of the agent and apply DRL to optimize the agent's decision making policy. We evaluate the performance against heuristic policies and show that our agent learns to act competitively without any prior knowledge.

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