Digital Library on Green Mobility


Electric Vehicle Charging Navigation Strategy in Coupled Smart Grid and Transportation Network: a Hierarchical Reinforcement Learning Approach

Publication Year: 2024

Author(s): Jiang C, Zhou L, Zheng J H, Shao Z


Existing EV charging navigation methods lack destination optimization, path planning, and real-time decision-making under uncertain factors, hindering efficient charging for electric vehicles. To address these problems, this paper first establishes a bilevel stochastic optimization model for EV charging navigation considering various uncertainties, and then proposes an EV charging navigation method based on the hierarchical enhanced deep Q network (HEDQN) to solve the above stochastic optimization model in real-time. The proposed HEDQN contains two enhanced deep Q networks, which are utilized to optimize the charging destination and charging route path of EVs, respectively. Finally, the proposed method is simulated and validated in two urban transportation networks. The simulation results demonstrate that compared with the Dijkstra shortest path algorithm, single-layer deep reinforcement learning algorithm, and traditional hierarchical deep reinforcement learning algorithm, the proposed HEDQN algorithm can effectively reduce the total charging cost of electric vehicles and realize online real-time charging navigation of electric vehicles, that shows excellent generalization ability and scalability.

Source of Publication: International Journal of Electrical Power & Energy Systems

Vol/Issue: 157: 109823

DOI No.: 10.1016/j.ijepes.2024.109823

Country: China

Publisher/Organisation: Elsevier

Rights: The Authors. Published by Elsevier Ltd.


Theme: Charging Infrastructure | Subtheme: Public charging station

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