Private versus Shared, Automated Electric Vehicles for U.S. Personal Mobility: Energy Use, Greenhouse Gas Emissions, Grid Integration, and Cost Impacts.

Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. While the convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, it requires careful integration with rapidly evolving electricity systems. Here, we examine these interactions using a U.

On-Demand Automotive Fleet Electrification Can Catalyze Global Transportation Decarbonization and Smart Urban Mobility.

Mobility on-demand vehicle (MODV) services have grown explosively in recent years, threatening targets for local air pollution and global carbon emissions. Despite evidence that on-demand automotive fleets are ripe for electrification, adoption of battery electric vehicles (BEVs) in fleet applications has been hindered by lack of charging infrastructure and long charging times. Recent research on electrification programs in Chinese megacities suggests that top-down policy targets can spur investment in charging infrastructure, while intelligent charging coordination can greatly reduce requirements for battery range and infrastructure, as well as revenue losses due to time spent charging.

Hybrid photoelectrochemical and photovoltaic cells for simultaneous production of chemical fuels and electrical power.

Harnessing solar energy to drive photoelectrochemical reactions is widely studied for sustainable fuel production and versatile energy storage over different timescales. However, the majority of solar photoelectrochemical cells cannot drive the overall photosynthesis reactions without the assistance of an external power source. A device for simultaneous and direct production of renewable fuels and electrical power from sunlight is now proposed.

Cost, Energy, and Environmental Impact of Automated Electric Taxi Fleets in Manhattan.

Shared automated electric vehicles (SAEVs) hold great promise for improving transportation access in urban centers while drastically reducing transportation-related energy consumption and air pollution. Using taxi-trip data from New York City, we develop an agent-based model to predict the battery range and charging infrastructure requirements of a fleet of SAEVs operating on Manhattan Island. We also develop a model to estimate the cost and environmental impact of providing service and perform extensive sensitivity analysis to test the robustness of our predictions.