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Deep Reinforcement Learning-Based Optimization of Mobile Charging Station and Battery Recharging Under Grid Constraints

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  • Atefeh Alirezazadeh

    (ConnectSmart Research Laboratory, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA)

  • Vahid Disfani

    (ConnectSmart Research Laboratory, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA)

Abstract

With the rise in traffic congestion, time has become an increasingly critical factor for electric vehicle (EV) users, leading to a surge in demand for fast and convenient charging services at locations of their choosing. Mobile Charging Stations (MCSs) have emerged as a new and practical solution to meet this growing need. However, the limited energy capacity of MCSs combined with the increasing volume of charging requests underscores the necessity for intelligent and efficient management. This study introduces a comprehensive mathematical framework aimed at optimizing both the deployment of MCSs and the scheduling of their battery recharging using battery swapping technology, while considering grid constraints, using the Deep Q-Network (DQN) algorithm. The proposed model is applied to real-world data from Chattanooga to evaluate its performance under practical conditions. The key goals of the proposed approach are to maximize the profit from fulfilling private EV charging requests, optimize the utilization of MCS battery packages, manage MCS scheduling without causing stress on the power grid, and manage recharging operations efficiently by incorporating photovoltaic (PV) sources at battery charging stations.

Suggested Citation

  • Atefeh Alirezazadeh & Vahid Disfani, 2025. "Deep Reinforcement Learning-Based Optimization of Mobile Charging Station and Battery Recharging Under Grid Constraints," Energies, MDPI, vol. 18(20), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:20:p:5337-:d:1768237
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    References listed on IDEAS

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