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| Title | Ai-Driven Dynamic Allocation And Management Optimization For Ev Charging Stations |
|---|---|
| ID_Doc | 7022 |
| Authors | Khan A.A.; Mahendran R.K.; Ullah F.; Ali F.; Bashir A.K.; Dabel M.M.A.; Omar M. |
| Year | 2025 |
| Published | IEEE Transactions on Intelligent Transportation Systems |
| DOI | http://dx.doi.org/10.1109/TITS.2025.3579924 |
| Abstract | The increasing acceptance of Electric Vehicles (EVs) leads to significant challenges for traditional forecasting methods due to external variables such as weather conditions, availability of renewable energy sources, and real-time traffic data. These factors affect the forecasting accuracy because of the unpredictable nature of renewable energy sources and weather. Conventional methods have limitations in terms of adapting dynamic conditions, leading to problems in allocating power and managing energy in EV Charging Stations (EVCS). To address these challenges, we propose a novel AI-driven approach called Dynamic Allocation and Management Optimization (DYNAMO), which integrates cutting-edge demand forecasting, power allocation, and efficiency-enhanced methods for smart city EV infrastructure. DYNAMO uses a Lite Transformer Gated Recurrent Unit (LT-GRU) for advanced demand prediction by considering critical factors like the number of incoming EVs, session duration, and station usage frequency. In LT-GRU, we integrate the strengths of transformer attention mechanism and sequential data processing of GRU to improve the prediction accuracy by capturing the long-term dependencies and prioritizing the important features even though in dynamic conditions. Additionally, an Intelligent Central Manager (ICM) groups EVCS into high, moderate, and low demand clusters, allowing for dynamic optimization of charging infrastructure. Furthermore, a Game Theory-based Deep Reinforcement Learning (GT-DRL) approach is employed, which considers variables such as vehicle demand, battery capacity, charging speed, and weather conditions, while preventing overloads and outages. Our approach not only enhances the operational efficiency of EVCS but also contributes to the development of more sustainable and reliable EV charging networks. Overall, the proposed framework’s ability to adapt in real-time ensures that it can support the increasing demand for EV infrastructure, minimize inefficiencies, and improve user experience. © 2000-2011 IEEE. |
| Author Keywords | Electric vehicles (EVs); intelligent central manager (ICM); intelligent transportation systems; LSTM; machine learning; resource allocation using dynamic clustering; security and privacy |
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