A comparative assessment of CO2 emission between gasoline, electric, and hybrid vehicles: A Well-To-Wheel perspective using agent-based modeling

Abstract

Road transports in the United States (U.S.) are heavily dependent on the production and consumption of fossil fuel. This high dependency on fossil fuels contributes significantly to carbon dioxide (CO2) emission, one of the leading Green House Gases (GHGs) responsible for global warming. Electrification of passenger vehicles could be an effective strategy to curb GHG emissions. Though Electric Vehicles (EVs) have zero tailpipe emissions, the power required to charge EV batteries may not necessarily come from carbon-free power plants. In this study, for a comprehensive comparison among EV, Plug-in Hybrid Electric Vehicle (PHEV), and Gasoline Vehicle (GV), we developed an agent-based simulation modeling framework for the entire energy pathway (Well-To-Wheel). As a case study, we estimated and compared CO2 emissions for different driving cycles for Texas utilizing 2018 electricity production mix data. Our simulation results revealed that for city driving cycles, EV performed environmentally better than GV and PHEV, but for highway driving cycles, EV underperformed compared to PHEV in all traffic conditions. For a combined driving cycle, PHEV performed better compared to EV and GV in moderate and low traffic conditions. Overall, according to the year 2018 energy mix data, PHEV is a better choice in Texas from an environmental perspective. Our sensitivity analysis showed that the environmental performance of an EV greatly depends on the percentage of renewable or clean energy in the overall grid electricity production mix. Our study will pave the way to conduct similar analyses for other states of the U.S, especially for the regions dependent highly on non-renewable energy sources. The research findings will help decision-makers design effective policies for EV and PHEV adoption to achieve maximum environmental benefits.