Can AI Abuse Personal Information in an EV Fast-Charging Market?

Abstract

In order to alleviate the range anxiety of electric vehicle users (EVUs), several researches focus on facilitating the efficiency of fast-electric vehicle charging stations (fast-EVCSs) using artificial intelligence (AI). This paper first proposes a fast-EVCS revenue maximization pricing policy using an AI approach, and we argue that the AI algorithm can learn to abuse EVUs information for maximizing its revenue. In order to investigate the hypothesis, firstly, a simulation environment is developed using vehicle performance models and an EVU’s charging station selection game. Then, we formulate the charging station revenue maximization problem as a Markov decision process (MDP) and propose a personalized dynamic pricing policy using a model-free reinforcement learning (RL) algorithm. From numerical simulation results, it is found that if the RL approach focuses solely on increasing revenue of the fast-EVCSs, it can learn to misuse personal information without any human intervention. To prevent such abuse, we suggest intuitive guidelines for policymakers and urban planners via numerical experiments.