Performance Analysis of Charging Infrastructure Sharing in UAV and EV-Involved Networks

Abstract

Electric vehicles (EVs) and unmanned aerial vehicles (UAVs) show great potential in modern transportation and communication networks, respectively. However, with growing demands for such technologies, the limited energy infrastructure becomes the bottleneck for their future growth. It might be of high cost and low energy efficiency for all the operators to each have their own dedicated energy infrastructure, such as charging stations. In this paper, we analyze a wireless charging infrastructure sharing strategy in UAV and EV-involved networks. We consider a scenario where UAVs can charge in EV charging stations and pay for the sharing fee. On the EVs' side, sharing infrastructure can earn extra profit but their service quality, such as waiting time, might slightly reduce. On the UAVs' side, if renting EV charging stations can achieve an acceptable system performance, say high coverage probability, while considering the cost, they may not need to build their dedicated charging stations. In this case, we use tools from stochastic geometry to model the locations and propose an optimization problem that captures the aforementioned trade-offs between cost or profit and quality of service. Our numerical results show that sharing infrastructure slightly increases the waiting time of EVs, say within 5 min, but dramatically decreases the waiting time of drones, say more than 50 min, and deploying more charging stations do achieve better performances, but all these better performances are expected to cost more.

A comprehensive state‐of‐the‐art review on reliability assessment and charging methodologies of grid‐integrated electric vehicles

“…Due to wireless communication networks, it will be feasible to interface cars with a communication system that allows communications capabilities between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I). A wide range of new opportunities will be opened up by the use of AI‐based algorithms, which will give vehicles some intelligence and ultimately significantly enhance transportation [109]. We can find a number of AI‐based proposals for many EV topics, including energy‐efficient routing, better and more intelligent charging, and battery temperature management [124].…”

“… $$${f}_{z}(y)=\frac{1}{y{\sigma }_{z}\sqrt{2\pi }}\left[\mathrm{exp}\left(-\frac{\left. {\left(\mathrm{ln}\,y-{\mu }_{z}\right)}^{2}\right)}{2{\sigma }_{z}^{2}}\right)\right]$$$ where the relevant shape parameters of the PDFs are σ x , μ x , σ z , μ z , improves reliability [109]. When using the MCS method to solve a problem, solutions are frequently built as mathematical forecasts of a particular random variable.…”