XL-Connect Project

31/12/2026

    Large scale system approach for advanced charging solutions.

    The number of battery-powered electric vehicles is expected to be at 30-40 million by 2030 in the EU. This strong increase of electric vehicles is a big challenge for the energy system in Europe, but at the same time a chance to use V1G/V2G/V2X-technologies. As vehicles are mainly parking, they can be used as energy storage in order to increase grid stability.

    The overall project objective is to optimize the entire charging chain - from energy provision to the end user - to create a clear benefit for all stakeholders. Therefore, a ubiquitous on-demand charging solution based on an optimized charging network considering human, technical and economic factors along the entire charging chain shall be developed.

    The investigation of the user behavior as well as the analysis of the energy system and grid will form the basis from a research side, to predict the future behavior of EV owners and fleet operators as well as possible shortcomings in the electric grid and energy system. The development of advanced charging technologies and control mechanisms as well as advanced charging and sector coupling concepts, will form the basis for the virtual and real evalulations/demonstrations conducted in 4 different European countries (Belgium, Germany, Italy, Portugal). In parallel a smart charging simulation environment (digital twin of the charging chain with a holistic simulation environment with multilevel component models and representative information flow between all agents) will be built up. This digital twin will incorporate the results of the demonstration actions and enable an upscaling to show the impact of these technologies. To ensure the interoperability and the optimization along this charging chain, the consortium comprises all relevant partners/stakeholders (energy providers, grid operators, charge point operator, EV equipment providers as well a vehicle manufacturer).

     

    This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement number 101056756.

     

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