e-Fuel: An EV-Friendly Urgent Electrical Charge-Sharing Model with Preference-Based Off-Grid Services

Abstract

Electric-powered vehicles (EVs) allow for an environmentally friendly and economic alternative to fuel-running ones. However, such an alternative is expected to impose further usage hikes and periods of instability on cities’ power systems. From their perspective, cities need to scale their infrastructure grids to allow for adequate power resources to feed such new power-hungry consumers. Indeed, for such a green alternative to proceed, our power grids need to be ready to cope with any unexpected hikes in the power consumption rates without compromising the stability of the services provided to our homes and workplaces. Operators’ steps in this path are still modest, and the coverage of EV charging stations is still insufficient as they are trying to avoid any further costs for upgrading their infrastructures. The lack of price consideration for the charging services offered at charging stations may result in EV drivers paying higher costs compared to traditional fuel vehicles to charge their EVs’ batteries, hindering the economic incentive of owning such sorts of vehicles. Hence, it may take a while for sufficient coverage to exist. Although for drivers the adoption of EVs represents a city-friendly alternative with affordable expenses, it usually comes with range anxiety and battery charging concerns. In this work, we are presenting e-Fuel, a charge-sharing model that allows for preference-based mobile EV charging services. In e-Fuel, we are proposing a stable weight-based vehicle-to-vehicle matching algorithm, through which drivers of EVs will be capable of requesting instant mobile charge-sharing service for their EVs. In addition to being mobile, such charging services are customized, as they are chosen based on the drivers’ preferences of price-per-unit, charging speed, and time of delivery. The developed e-Fuel matching algorithm has been tested in various environments and settings. Compared to the benchmark price-based matching algorithm, the resulting matching decisions of e-Fuel come with balanced matching attributes that mostly allow for 6- to 7-fold shorter service delivery times for a minimal increase in service charges that vary between 9% and 65%.