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.

翻译：里程焦虑和长时间充电仍然是电动汽车普及的关键障碍。动态感应充电通过在行驶过程中实现无线电力传输，提供了一种引人注目的解决方案，有可能减少电池尺寸需求，从而降低车辆成本。然而，动态感应充电基础设施成本高昂且功率受限，需要智能资源分配以最大化用户满意度和经济可行性。我们提出了一个模型预测控制框架，用于动态感应充电系统中的最优功率分配，利用边缘计算和车辆通信来优先处理电池状态危急的车辆。该框架通过基于SUMO的仿真在土耳其伊斯坦布尔一个10公里真实城市场景中实现和评估，考虑了不同的交通强度。结果揭示了未协调分配的两个关键局限性。首先，当需求饱和系统容量时，即使有可用功率，资源利用率仍然欠优。其次，当需求超过容量时，功率均匀分配会导致大量电池电量严重不足的车辆，这些车辆可能需要紧急停车。我们基于MPC的策略应对了这两种情况——在饱和状态下通过动态条带再平衡最大化功率利用率，在稀缺状态下通过优先处理耗尽电池（牺牲电量充足的车辆）来提高满意度公平性。该框架和仿真工具已作为开源发布，以支持这一新兴领域的进一步研究。