Optimal Dynamic Recharge Scheduling for Two-Stage Wireless Power Transfer
Optimal Dynamic Recharge Scheduling for Two-Stage Wireless Power Transfer
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DOI：
10.1109/tii.2020.3035645
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发表时间：
2021-08
影响因子：
12.3
通讯作者：
A. Y. Pandiyan;D. Boyle;M. Kiziroglou;S. Wright;E. Yeatman
中科院分区：
文献类型：
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作者：
A. Y. Pandiyan;D. Boyle;M. Kiziroglou;S. Wright;E. Yeatman
许多工业物联网应用需要自主操作，并将设备集成在不可访问的位置。无线电力传输（WPT）和自动驾驶汽车技术的最新进展相结合，有可能解决许多与嵌入式设备的维护和数据收集有关的遗留问题。为廉价的无人机（UAV）和嵌入式设备配备子系统以促进WPT允许UAV成为可行的移动的电力输送车辆（PDV）和数据收集代理。因此，一个关键的挑战是确保PDV可以最佳地调度整个网络的电力输送，使得它尽可能可靠和资源高效。为了实现这一目标，并超越天真的按需充电策略，在这篇文章中，我们提出了一个两阶段的无线电力网络（WPN）的方法，其中一个大型网络的设备可以被分组为小集群，其中能量分组感应传递到每个集群由PDV声学分布到集群内的设备。在这篇文章中，我们描述了一种新的动态充电调度算法，结合遗传加权聚类与最近邻搜索，共同最小化PDV行驶距离和WPT损失。该算法的有效性和性能进行评估，在模拟实验得出的痕迹，该算法被证明可以实现90%的吞吐量为大型，密集的网络。
Many industrial-Internet-of-Things applications require autonomous operation and incorporate devices in inaccessible locations. Recent advances in wireless power transfer (WPT) and autonomous vehicle technologies, in combination, have the potential to solve a number of residual problems concerning the maintenance of, and data collection from embedded devices. Equipping inexpensive unmanned aerial vehicles (UAV) and embedded devices with subsystems to facilitate WPT allows a UAV to become a viable mobile power delivery vehicle (PDV) and data collection agent. A key challenge is, therefore, to ensure that a PDV can optimally schedule power delivery across the network, such that it is as reliable and resource efficient as possible. To achieve this and out-perform naive on-demand recharging strategies, in this article, we propose a two-stage wireless power network (WPN) approach in which a large network of devices may be grouped into small clusters, where packets of energy inductively delivered to each cluster by the PDV are acoustically distributed to devices within the cluster. In this article, we describe a novel dynamic recharge scheduling algorithm that combines genetic weighted clustering with nearest neighbor search to jointly minimize PDV travel distance and WPT losses. The efficacy and performance of the algorithm are evaluated in simulation using experimentally derived traces, and the algorithm is shown to achieve $\sim$90% throughput for large, dense networks.