Deep reinforcement learning-based eco-driving control for connected electric vehicles at signalized intersections considering traffic uncertainties

Jie Li, Abbas Fotouhi, Wenjun Pan, Yonggang Liu, Yuanjian Zhang and Zheng Chen

Energy, 2023, vol. 279, issue C

Abstract: Eco-driving control poses great energy-saving potential at multiple signalized intersection scenarios. However, traffic uncertainties can often lead to errors in ecological velocity planning and result in increased energy consumption. This study proposes an eco-driving approach with a hierarchical framework to be leveraged at signalized intersections that considers the impact of traffic uncertainty. The proposed approach leverages a queue-based traffic model in the upper level to estimate the impact of traffic uncertainty and generate dynamic modified traffic light information. In the lower level, a deep reinforcement learning-based controller is constructed to optimize velocity subject to the constraints from the traffic lights and traffic uncertainty, thereby reducing energy consumption while ensuring driving safety. The effectiveness of the proposed control strategy is demonstrated through numerous simulation case studies. The simulation results show that the proposed method significantly improves energy economy and prevents unnecessary idling in uncertain traffic scenarios, as compared to other approaches that ignore traffic uncertainty. Furthermore, the proposed method is adaptable to different traffic scenarios and showcases energy efficiency.

Keywords: Eco-driving; Deep reinforcement learning; Velocity optimization; Signalized intersection; Connected electric vehicle (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:279:y:2023:i:c:s0360544223015335

DOI: 10.1016/j.energy.2023.128139

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