V2VDisCS: Vehicle to Vehicle Distributed Charge Sharing in Intelligent Transportation Systems

Abstract

Electric Vehicles (EVs) have become popular in the domain of Intelligent Transportation Systems for their ability to mitigate increasing environmental concerns by reducing carbon footprints and conserving fossil fuels. Due to the scarcity of static charging stations, Vehicle-to-Vehicle (V2V) charge sharing can facilitate the on-demand charging requirement of EVs. However, most of the V2V charge-sharing solutions are either centralized or semi-centralized, causing long waiting times, huge message overhead, and high infrastructural costs. For a large network, assigning a suitable donor EV for an acceptor EV as well as maximizing the matching cardinality in a distributed environment is a challenging problem. In this paper, the problem of V2V matching for charge sharing is mapped to the classical stable matching problem in bipartite graphs. The problem is formulated using integer linear programming that considers flexible decision making for EVs based on multiple charging criteria and constraints. However, as EVs have limited communication ranges, an EV can’t possess knowledge about the entire vehicular network. So we propose two sets of distributed heuristics under the name of Vehicle to Vehicle Distributed Charge Sharing (V2VDisCS), which yield a sub-optimal solution with lower computational and message complexities compared to existing distributed solutions. We analyze the average case matching probabilities and prove the sub-optimality of our approach. Simulation studies show that our heuristics outperform the existing distributed approaches in terms of message overhead and matching percentage. They show a comparable result for matching preference with respect to the standard centralized stable matching algorithm.