Intelligent Vehicle-to-Vehicle Charging Navigation for Mobile Electric Vehicles via VANET-Based Communication

Abstract

A direct vehicle-to-vehicle (V2V) charging scheme supplies flexible and fast energy exchange way for electric vehicles (EVs) without the supports of charging stations. Main technical challenges in cooperative V2V charging may include the efficient charging navigation structure designs with low communication loads and computational complexities, the decision-making intelligence for the selection of stopping locations to operate V2V charging services, and the optimal matching issue between charging EVs and discharging EVs. In this paper, to solve the above problems, we propose an intelligent V2V charging navigation strategy for a large number of mobile EVs. Specifically, by means of a hybrid vehicular ad-hoc networks (VANETs) based communication paradigm, we first study a mobile edge computing (MEC) based semi-centralized charging navigation framework to ensure the reliable communication and efficient charging coordination. Then, based on the derived charging models, we propose an effective local charging navigation scheme to adaptively select the optimal traveling route and appropriate stopping locations for mobile EVs via the designed Q-learning based algorithm. After that, an efficient global charging navigation mechanism is proposed to complete the best charging-discharging EV pair matching based on the constructed weighted bipartite graph. A series of simulation results and theoretical analyses are presented to demonstrate the feasibility and effectiveness of the proposed V2V charging navigation strategy.