Neuracharge-CIL - Charging Intelligence Layer

Most existing smart charging solutions operate reactively and focus on short-term charging demands or electricity prices, without forward-looking planning or system-wide optimization across multiple sites or energy communities. As a result, efficiency potentials remain unused, power grids are unnecessarily stressed, and charging infrastructure is often expanded in a costly and suboptimal manner. This project addresses these limitations by developing a Charging Intelligence Layer, an intelligent control system that integrates predictive planning, simulation, optimization, and real-time control for entire charging systems. A key foundation is a patent-pending AI-based forecasting approach that predicts charging plans based on individual user behavior with a planning horizon of up to 14 days. A graph-based digital twin represents the complete charging infrastructure and serves as the basis for simulations, scenario analyses, and system-wide optimization. Building on this, aggregated forecasts and consistent charging schedules are generated that consider energy costs, CO₂ emissions, grid constraints, and user requirements. An adaptive real-time control mechanism adjusts plans in a targeted and minimal way when deviations occur. The project aims to make a measurable contribution to the mobility and energy transition by enabling grid-friendly operation, more efficient use of renewable energy, and energy and infrastructure cost savings of 20 to 30 percent, while remaining compatible with existing charge point management systems.