CognitiveCharge
CognitiveCharge
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认知充电
DOI：
10.1145/3213299.3213301
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Radenkovic M
中科院分区：
文献类型：
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作者：
Radenkovic M
电动汽车（EV）正迅速变得越来越普遍，未来几年全球范围内的所有权将增加。电动汽车的增加对电网的潜在影响已经得到了广泛的研究，在目前的状态下，现有的电网基础设施将难以满足高峰充电时间的高需求。电动汽车的有限范围使这个问题更加复杂。因此，我们提出了认知充电，一种新的方法来预测和自适应断开意识的机会主义能源发现和转移的智能车辆充电。CognitiveCharge通过使用隐式预测混合联系和资源拥塞策略来检测并响应有耗尽风险的单个节点和网络区域。CognitiveCharge利用基于局部相对效用的方法，自适应地将能量从具有能量盈余的网络部分卸载到具有非均匀耗尽率的耗尽区域。我们使用美国旧金山弗朗西斯科和英国诺丁汉的多日跟踪来评估CognitiveCharge，以在一系列指标上与现有基础设施进行比较。CognitiveCharge成功地消除了自组织和基础设施充电点的拥堵，减少了车辆从确定需要能源的点开始充电所需的时间，并大幅减少了评估期间需要能源的节点总数。
Electric vehicles (EVs) are rapidly becoming more common and ownership is set to rise globally in coming years. The potential impacts of increased EVs on the electrical grid have been widely investigated and in its current state, existing grid infrastructure will struggle to meet the high demands at peak charging hours. The limited range of electric cars compounds this issue. We therefore propose CognitiveCharge, a novel approach to predictive and adaptive disconnection aware opportunistic energy discovery and transfer for the smart vehicular charging. CognitiveCharge detects and reacts to individual nodes and network regions which are at risk of getting depleted by using implicit predictive hybrid contact and resources congestion heuristics. CognitiveCharge exploits localised relative utility based approach to adaptively offload the energy from parts of the network with energy surplus to depleting areas with non-uniform depletion rates. We evaluate CognitiveCharge using a multi-day traces for the city of San Francisco, USA and Nottingham, UK to compare against existing infrastructure across a range of metrics. CognitiveCharge successfully eliminates congestion at both ad hoc and infrastructure charging points, reduces the time that a vehicle must wait to charge from the point at which it identifies as being in need of energy, and drastically reduces the total number of nodes in need of energy over the evaluation period.