mlsanityData to 14 Oct 2026

Reinforcement Learning for Hybrid Charging Stations Planning and Operation Considering Fixed and Mobile Chargers

arXiv 2506.16764 ↗First posted 20 Jun 2025cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

The success of vehicle electrification relies on efficient and adaptable charging infrastructure. Fixed-location charging stations often suffer from underutilization or congestion due to fluctuating demand, while mobile chargers offer flexibility by relocating as needed. This paper studies the optimal planning and operation of hybrid charging infrastructures that combine both fixed and mobile chargers within urban road networks. We formulate the Hybrid Charging Station Planning and Operation (HCSPO) problem, jointly optimizing the placement of fixed stations and the scheduling of mobile chargers. A charging demand prediction model based on Model Predictive Control (MPC) supports dynamic decision-making. To solve the HCSPO problem, we propose a deep reinforcement learning approach enhanced with heuristic scheduling. Experiments on real-world urban scenarios show that our method improves infrastructure availability - achieving up to 244.4% increase in coverage - and reduces user inconvenience with up to 79.8% shorter waiting times, compared to existing solutions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 31 references that are in the map. First posted on arXiv 20 Jun 2025.

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4 of 31 references.

References · 4

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2017Graph Attention Networkscited by 8,923
  3. 2018Temporal Pattern Attention for Multivariate Time Series Forecastingcited by 125
  4. 2023A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand predictioncited by 9

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  3. 2024Advancements in Electric Vehicle Charging Optimization: A Survey of Reinforcement Learning Approaches
  4. 2022Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning
  5. 2025Electric Bus Charging Schedules Relying on Real Data-Driven Targets Based on Hierarchical Deep Reinforcement Learning
  6. 2025Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
  7. 2026Few-Shot Learning for Dynamic Operations of Automated Electric Taxi Fleets under Evolving Charging Infrastructure: A Meta-Deep Reinforcement Learning Approach
  8. 2025Dynamic Load Balancing for EV Charging Stations Using Reinforcement Learning and Demand Prediction