Coupled Transport-Energy Systems for E-Mobility: Dynamic Modeling and AI-Based Optimization

Research Project, 2027 – 2028

The rapid growth of electric vehicles (EVs) is transforming road transport but also creates new challenges for both transport and energy systems. Large-scale EV charging can overload local power grids, leading to congestion, voltage deviations, and reliability risks. Electric transport systems also depend heavily on electricity supply, meaning power outages or extreme weather events can directly disrupt mobility. In addition, charging infrastructure remains insufficient and often poorly planned, particularly for freight transport. Most existing studies analyze and optimize transport and energy systems separately, which can lead to suboptimal solutions and limited understanding of their interdependencies.

This project addresses these gaps by developing a coordinated and scalable approach that couples transport and energy system modeling and decision-making. It will create a high-resolution simulation environment capturing dynamics and interactions among EV users, charging infrastructure, mobility demand, and power grid operations. On top of this environment, a deep-learning-based multi-agent reinforcement learning will be developed to enable decentralized and coordinated decisions on EV user charging decisions, infrastructure deployment, and energy system operation. Using the approaches, the project will analyze how synergistic charging of passenger vehicles and heavy-duty electric vehicles, combined with distributed renewable energy and battery storage, can improve system efficiency and resilience.