Optimal Incentive Decision-making Scheme for Charging Electric Vehicles in Internet of Things: A Stackelberg Game Aproach

Abstract

With the help of Internet of things (IoT), many more advantages have easily been brought to electric vehicle (EV) users supplied from mobile charging stations (MCSs). However, through the existing methods, the charging problems cannot be solved with many different constraints, i.e., the finite power supply and the dynamic arrival of EV users. In this paper, we propose a four-stage Stackelberg game scheme to study how to charge EV users in MCSs. Firstly, this charging system composed of EV users, MCS operators (MCSOs) and smart grid operators (SGOs) in IoT is considered. Due to the trading relationship among them, the utility functions of them are designed when bit error ratio (BER) as an important performance criterion in IoT is also taken into account, leading to the uncertainty of power demand. Then, through the backward induction method, we obtain the optimal strategy while the existence of Stackelberg equilbrium (SE) is analyzed and proved. Considering the complex computation, an iterative search algorithm for SE in this four-stage game scheme is presented. Finally, simulation results validate the effectiveness of our proposal and each of them in this charging system can be benefited.