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July 24, 2026Scientific ReportsOpen Access

Adaptive primal–dual Q-learning for electric vehicle route optimization on real-world charging networks

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Authors

SKSarvesh Ram KumarAmrita Vishwa VidyapeethamRRRayappa David Amar RajAmrita Vishwa VidyapeethamAPArchana PallakondaNational Institute of Technology Warangal

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Implication

Randomized trial demonstrates efficient route planning in electric vehicles, suggesting enhanced navigation strategies.

Key Points

  • This research aims to develop an effective route optimization framework for electric vehicles using adaptive primal-dual Q-learning techniques.
  • Integrates reinforcement learning with graph-based approaches for EV navigation.
  • Utilizes a Dual Q-Adaptive Weighting model for dynamic reward-cost balance.
  • Constructs a navigation graph using real-world EV charging data from AFDC and Placekey datasets.
  • Achieved a route accuracy of 78.66% with the Dual Q-Adaptive model.
  • Outperformed standard Q-Learning (77.52%) and Double Q-Learning (76.27%) models.
  • Traditional A* and Dijkstra algorithms achieved accuracy rates of 74.26% and 60.92%, respectively.

Cite This Study

Kumar et al. (2026) studied this question.

synapsesocial.com/papers/6a6300f2395161722cd15bc3https://doi.org/10.1038/s41598-026-49124-8
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