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

Atefeh Alirezazadeh () and Vahid Disfani
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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

Energies, 2025, vol. 18, issue 20, 1-21

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.

Keywords: mobile charging station; electric vehicle charging scheduling; battery charging station; energy management; deep Q-Networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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