Abstract
The fast-growing popularity of electric vehicles (EVs) poses complex challenges for the existing power grid infrastructure to meet the high demands at peak charging hours. Discovering and transferring energy amongst EVs in mobile vehicular edges and fogs is expected to be an effective solution for bringing energy closer to where the demand is and improving the scalability and flexibility compared to traditional charging solutions. In this paper, we propose a fully-distributed energyaware opportunistic charging approach which enables distributed multi-layer adaptive edge cloud platform for sustainable mobile autonomous vehicular edges which host dynamic on-demand virtual edge containers of on-demand services. We introduce a novel Reinforcement Learning (Q-learning) based SmartCharge algorithm formulated as a finite Markov Decision Process. We define multiple edge energy states, transitions and possible actions of edge nodes in dynamic complex network environments which are adaptively resolved by multilayer real-time multidimensional predictive analytics. This allows SmartCharge edge nodes to more accurately capture, predict and adapt to dynamic spatial-temporal energy supply and demand as well as mobility patterns when energy peaks are expected. More specifically, SmartCharge edge nodes are able to autonomously and collaboratively understand when (how soon) and where the geo-temporal peaks are expected to happen, thus enable better local prediction and more accurate global distribution of energy resources. We provide multi-criteria evaluation of SmartCharge against competitive protocols over real-world San Francisco Cab mobility traces and in the presence of real-world users' energy interest traces driven by Foursquare San Francisco dataset. We show that SmartCharge successfully predicts and mitigates congestion in peak charging hours, reduces the waiting time between vehicles sending energy demand requests and being successfully charged as well as significantly reduces the total number of vehicles in need of energy.