Electric Vehicle
Electric vehicles (EVs) are a key element in transitioning to sustainable transportation, driving research focused on optimizing their integration into existing energy infrastructure and improving their operational efficiency. Current research emphasizes developing sophisticated charging strategies using techniques like multi-agent reinforcement learning, deep learning for energy consumption prediction, and graph neural networks for spatial optimization of charging infrastructure, often incorporating real-world constraints and uncertainties. These advancements aim to address challenges such as grid stability, range anxiety, and efficient energy management, ultimately contributing to a more sustainable and reliable transportation sector.
Papers
Decentralized Smart Charging of Large-Scale EVs using Adaptive Multi-Agent Multi-Armed Bandits
Sharyal Zafar, Raphaël Feraud, Anne Blavette, Guy Camilleri, Hamid Ben
Forecasting Battery Electric Vehicle Charging Behavior: A Deep Learning Approach Equipped with Micro-Clustering and SMOTE Techniques
Hanif Tayarani, Trisha V. Ramadoss, Vaishnavi Karanam, Gil Tal, Christopher Nitta