Utilizing Transportation Infrastructure, Big Data and AI for Localized EV Energy Neutrality

Abstract

The transportation sector in the US consumes 28% of total energy consumption for transporting people and goods. Roughly 92% of transportation is fueled by gasoline, diesel and jet fuel, with electricity providing less than 1% of transportation energy (2018) [1]. The introduction of electric vehicles (EVs) on the road is growing at the rate of 40% Y-o-Y [2], leading to ever increasing demand on grid supplied energy to recharge these EVs. 60% of grid energy is generated from nonrenewable (NR) sources (coal & natural gas). Expending NR sources to service EVs is neither sustainable nor desirable when long term effects are considered. Since EVs are still in their initial stages of mass adoption in the US, an updated and scalable approach to servicing the growing EV energy need is required. One such approach in which existing transport infrastructure (gas stations, roads, trucks etc) are multi-purposed with scalable, feasible modifications is presented in this paper. Furthermore the possibility of optimizing the energy capture-storage-usage (CSU) cycle using big-data and artificial intelligence (AI) is also explored.