Scheduling a Fleet of Dynamic EV Chargers for Maximal Profile

The proliferation of electric vehicles (EVs) faces obstacles like range anxiety and inadequate charging infrastructure. To address these challenges, dynamic EV-to-EV charging technology has emerged. This innovative method enables one EV with surplus battery to charge another EV while both are in motion. This study focuses on efficiently pairing and routing energy suppliers (ESs) to meet energy requesters (ERs) and transfer energy via platooning. The key objective is to manage the ES fleet effectively, framed as a vehicle routing problem, to maximize profit by serving as many energy requests as possible. We formulate the problem as an integer programming model within a time-space network and propose a local search-based heuristic algorithm designed to efficiently handle large-scale networks. Numerical experiments conducted on Sioux Falls validate the efficacy of our approach, allowing for an assessment of algorithm performance under realistic large-scale conditions. The findings illustrate enhancements in ER travel time and energy overhead, alongside maximized profits for ESs.