A MEC-Based Optimization Framework for Dynamic Inductive Charging

TL;DR

This paper introduces a Model Predictive Control framework for optimizing power allocation in dynamic inductive charging of electric vehicles, enhancing efficiency and fairness through edge computing and vehicular communication.

Contribution

It presents a novel MPC-based resource allocation method for DIC systems, addressing efficiency and fairness issues in a realistic urban simulation environment.

Findings

Uncoordinated allocation leads to suboptimal resource utilization.

Demand exceeding capacity causes critically unsatisfied vehicles with emergency stop risk.

The proposed MPC strategy improves power utilization and fairness in various traffic scenarios.

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.