A MEC-Based Optimization Framework for Dynamic Inductive Charging

2026-04-30

AI Summary

This study addresses the low user satisfaction and poor economic efficiency of dynamic wireless charging systems, which stem from high costs, power limitations, and the absence of intelligent scheduling mechanisms. To overcome these challenges, this work proposes a novel framework that integrates Model Predictive Control (MPC) with Multi-access Edge Computing (MEC) to enable vehicle-infrastructure cooperative dynamic power allocation. The approach prioritizes charging for vehicles with critically low battery levels while simultaneously optimizing power utilization under saturated conditions and ensuring fair resource distribution during scarcity. Simulation results based on a 10-kilometer urban road network in Istanbul, implemented in SUMO, demonstrate that the proposed method significantly improves charging power utilization, effectively reduces the proportion of severely undercharged vehicles, and mitigates the risk of emergency stops.

Abstract

Range anxiety and long recharging times remain critical barriers to electric vehicle adoption. Dynamic Inductive Charging (DIC) offers a compelling solution by enabling wireless power transfer while driving, potentially reducing battery size requirements and thus vehicle costs. However, DIC infrastructures are expensive and power-constrained, requiring intelligent resource allocation to maximize user satisfaction and economic viability. We propose a Model Predictive Control framework for optimal power allocation in DIC systems, using edge computing and vehicular communications to prioritize vehicles with critical battery states. The framework is implemented and evaluated through SUMO-based simulations on a realistic 10 km urban scenario in Istanbul, Turkey, under varying traffic intensities. Results demonstrate two critical limitations of uncoordinated allocation. First, resource utilization remains suboptimal despite available power when demand saturates system capacity. Second, when demand exceeds capacity, uniform distribution of power leaves a heavy tail of critically unsatisfied vehicles that may require emergency stops. Our MPC-based strategy addresses both regimes -- maximizing power utilization during saturation through dynamic stripe rebalancing, and improving satisfaction fairness under scarcity by aggressively prioritizing depleted batteries at the expense of well-charged vehicles. The framework and simulation tools are released as open-source to support further research in this emerging domain.

Problem

• Dynamic Inductive Charging
• resource allocation
• electric vehicles
• power constraints
• user satisfaction

Innovation

• Model Predictive Control
• Dynamic Inductive Charging
• Edge Computing
• Resource Allocation
• Electric Vehicles