An Electrifying Framework for the Future of Transport Optimizing Electric Vehicle Charging Infrastructure for Enhanced Adoption

Abstract

As electric vehicles begin to enter price points that are competitive with internal combustion engine vehicles, a greater dialogue must be had surrounding their lack of adoption. Despite monetary incentives from the government, environmental considerations, and technological advancements electric vehicles are continuously under-adopted in comparison to both their gasoline-powered and hybrid counterparts. Persistent worries about inadequate infrastructure to support electric vehicles dominate the public conscience. These faults necessitate a robust approach to the optimization of electric vehicle charging locations which considers existing travel behaviors such that the complexities of owning an electric vehicle do not have as adverse of an effect on their potential adopters, as they would otherwise. By deploying a genetic algorithm which randomly samples properties throughout Boston, this research assesses the viability of each potential charging location under a set of criteria concerning the proposed location's popularity as a destination for trips with dwell times sufficient enough to charge an electric vehicle, accessibility to nearby amenities, number of trips generated by the property, maximal distance from other proposed chargers, as well as maximal distance from residences with the capacity to charge their vehicle from home. Within the subsequent analysis, we find that the optimized charging locations do indeed align with these idealized hypothetical locations, suggesting that through the deployment of the framework devised in this research, charging locations can be situated optimally into the existing travel behaviors of individuals. Refinements in charging infrastructure allocation as proposed by the methods of this research, are conducive to enhanced adoption rates of electric vehicles as these optimal charging locations situate themselves equitability amongst the existing travel trends of drivers.