A MEC-Based Optimization Framework for Dynamic Inductive Charging
Edge-based power allocation reduces emergency stops from uneven charging in DIC systems.
Dynamic Inductive Charging (DIC) promises to eliminate range anxiety by wirelessly charging EVs while driving, but its high cost and power constraints demand intelligent resource allocation. Uncoordinated power distribution leads to suboptimal utilization when demand saturates capacity and leaves a heavy tail of critically low-battery vehicles during scarcity, risking emergency stops.
To address this, researchers from multiple institutions developed a Model Predictive Control (MPC) framework leveraging edge computing (MEC) and vehicular communications. It dynamically rebalances power stripes under saturation and aggressively prioritizes depleted batteries during overload. Tested in a realistic 10 km urban Istanbul scenario with SUMO simulations, the approach significantly improves satisfaction fairness and resource efficiency. The open-source release of the framework and tools enables further research in this emerging domain.
- Uses MPC combined with MEC (Mobile Edge Computing) and vehicular communication for real-time power allocation.
- Tested in a SUMO simulation of a 10 km urban route in Istanbul under varying traffic intensities.
- Reduces the heavy tail of critically unsatisfied vehicles that would otherwise require emergency stops.
Why It Matters
Edge-optimized dynamic charging could slash EV battery costs and eliminate range anxiety in dense urban areas.