Reinforcement learning for electric vehicle charging scheduling: A systematic review

Zhonghao Zhao, Carman K.M. Lee, Xiaoyuan Yan and Haonan Wang

Transportation Research Part E: Logistics and Transportation Review, 2024, vol. 190, issue C

Abstract: As climate change and environmental concerns have become increasingly pressing issues, electric vehicles (EVs) have emerged as a viable and environmentally-friendly alternative to their traditional gasoline-powered counterparts. With the growing popularity of EVs, it is widely recognized that scheduled EV charging is of vital importance in enhancing users’ satisfaction, improving the profitability of charging stations, and ensuring the secure operation of power grids. Therefore, effective charging scheduling is crucial to achieve EV integration into modern mobility and further promote mass EV adoption. However, EV charging scheduling problems (CSPs) present a significant challenge to conventional methods owing to the complex nature of the decision environment. Reinforcement learning (RL) has gained considerable attention for addressing various CSPs with a Markov decision process (MDP) formulation. This paper aims to conduct a systematic review of existing studies that utilize RL to tackle CSPs. Firstly, we present a summary of the state-of-the-art CSPs and RL algorithms, followed by a review of previous research studies and a discussion of the current challenges in this field. Finally, several unresolved issues and possible future research topics are identified based on our review findings.

Keywords: Electric vehicle; Charging scheduling; Reinforcement learning; Charging station; Literature review (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.tre.2024.103698

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