This repository stores the data of the NCST project titled "EVALUATE: Electric Vehicle Assessment and Leveraging of Unified models toward AbatemenT of Emissions, Phase II." The abstract is as follows.
The NCST EVALUATE (Electric Vehicle Assessment and Leveraging of Unified models toward AbatemenT of Emissions) project (Phases I and II) develops a rigorous methodology involving a high-fidelity system of systems model (i.e., vehicle powertrain, EV charging profiles and grid dispatch datasets) for the purpose of forecasting the emissions outputs of a class of vehicles and use cases. The Phase I findings explored light duty vehicles (LDV) typical urban commuters and households that operate LDVs for daily personal use. Phase II, presented here, focuses on a series of targeted case studies that extend prior work from LDVs operated by individuals to service-oriented vehicles operated by small and medium businesses. Vehicles used in the present study as representative public service fleets include the following pickup trucks, vans, Medium Duty (MD) delivery vehicles, and refuse trucks. In one of the study’s simulations for a MD use case where a specific marginal grid generating resource is selected on an hourly basis to meet a particular EV charging event, estimated CO2 emissions could be as much as 42% lower than a conventional gasoline vehicle, or as much as 24% higher than a conventional gasoline vehicle. This large variance is purely a function of when and how quickly the vehicle is recharged. This study reveals key insights as follows. (1) higher temporal resolution is important to develop more accurate estimates of EV CO2 emissions. Along with this, EV charge management is imperative for all use cases, and has profound implications on infrastructure and emissions; (2) Hybrid Electric Vehicles (HEVs) often performed as well as EVs in contemporary simulations on the basis of emissions benefits, suggesting that consideration of an array of vehicle technologies is important; (3) there is a growing need to focus on higher rate EV charging applications (e.g., DCFC), and related implications on energy storage, as proxied by large vehicle batteries; (4) The trend toward increasing electrification of the transportation sector will continue in conjunction with electrification across other sectors (e.g., buildings, data centers, industry). As such associated cross-sector planning and study of concomitant emissions must be considered in context of other grid trends. Primary contributions of this effort are the development of new methodologies, integration of sub-system models and independent data sources, and decision support tools that estimate the environmental impacts of vehicle electrification. The study’s methodologies and use cases can enhance understanding and scale up in additional EV-grid applications, sectors and regions.