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
Investigating the Spatiotemporal Charging Demand and Travel Behavior of Electric Vehicles Using GPS Data: A Machine Learning Approach
Sina Baghali, Zhaomiao Guo, Samiul Hasan
Defining a synthetic data generator for realistic electric vehicle charging sessions
Manu Lahariya, Dries Benoit, Chris Develder