E Mobility

E-mobility research focuses on optimizing the design, implementation, and management of electric transportation systems to address environmental concerns and improve urban mobility. Current research emphasizes data-driven modeling of energy consumption, particularly for micromobility, using machine learning algorithms to improve accuracy and prediction. Furthermore, research utilizes multi-agent reinforcement learning and deep neural search to optimize fleet rebalancing, infrastructure deployment, and the integration of shared autonomous electric vehicles, aiming to enhance efficiency and user experience. These advancements have significant implications for urban planning, transportation policy, and the development of sustainable transportation solutions.