CatCharger: Deploying In-Motion Wireless Chargers in a Metropolitan Road Network via Categorization and Clustering of Vehicle Traffic

In metropolitan areas with heavy transit demands, electric vehicles (EVs) are expected to be continuously driving without recharging downtime. Wireless power transfer (WPT) provides a promising solution for in-motion EV charging. Nevertheless, previous works are not directly applicable for the deployment of in-motion wireless chargers due to their different charging characteristics. The challenge of deploying in-motion wireless chargers to support the continuous driving of EVs in a metropolitan road network with the minimum cost remains unsolved. We propose CatCharger to tackle this challenge. By analyzing a metropolitan-scale data set, we found that traffic attributes like vehicle passing speed, daily visit frequency at intersections (i.e., landmarks), and their variances are diverse, and these attributes are critical to in-motion wireless charging performance. Driven by these observations, we first group landmarks with similar attribute values using the entropy minimization clustering method, and select candidate landmarks from the groups with suitable attribute values. Then, we use the kernel density estimator (KDE) to deduce the expected vehicle residual energy at each candidate landmark and consider EV drivers’ routing choice behavior in charger deployment. Finally, we determine the deployment locations by formulating and solving a multiobjective optimization problem, which maximizes vehicle traffic flow at charger deployment positions while guaranteeing the continuous driving of EVs at each landmark. Trace-driven experiments demonstrate that CatCharger increases the ratio of driving EVs at the end of a day by 12.5% under the same deployment cost.

在交通需求大的大都市地区，电动汽车（EV）有望在无需充电停机的情况下持续行驶。无线电力传输（WPT）为电动汽车动态充电提供了一种有前景的解决方案。然而，由于其不同的充电特性，以往的研究成果不能直接应用于动态无线充电器的部署。在大都市道路网络中以最低成本部署动态无线充电器以支持电动汽车持续行驶这一挑战仍未解决。我们提出了CatCharger来应对这一挑战。通过分析一个大都市规模的数据集，我们发现车辆通过速度、路口（即地标）的日访问频率及其方差等交通属性是多样的，并且这些属性对动态无线充电性能至关重要。受这些观察结果的驱动，我们首先使用熵最小化聚类方法将具有相似属性值的地标分组，并从具有合适属性值的组中选择候选地标。然后，我们使用核密度估计器（KDE）来推断每个候选地标处的预期车辆剩余能量，并在充电器部署中考虑电动汽车驾驶员的路线选择行为。最后，我们通过制定和求解一个多目标优化问题来确定部署位置，该问题在保证每个地标处电动汽车持续行驶的同时，使充电器部署位置的车辆交通流量最大化。基于轨迹的实验表明，在相同的部署成本下，CatCharger使一天结束时行驶的电动汽车比例提高了12.5%。