CognitiveCharge

Abstract

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.