Research Examines Charging Flexibility of Shared Automated Electric Vehicles


The transportation industry is undergoing a major transformation, via vehicle electrification, automation, and sharing undertakings. And, shared automated electric vehicles (SAEV) is at the intersection of these trends.

Whilst shared automated electric vehicles offer possibilities of lower costs, reduced emissions, and better travel experience. On the other hand, as the SAEV market expands, uncoordinated charging of large number of electric vehicles could burden the electric grids and increase costs.

To alleviate these issues, a team of researchers from the National Renewable Energy Laboratory examined the potential charging flexibility of future SAEVs, now nascent.

To determine this, the team furnished travel demand for four major U.S. cities: Detroit, Washington, Miami, and Austin. Thereafter, the team used a coordinated charging model aimed to reduce fleet charging costs in response to electricity rates that vary over time, assuming likelihood of five different electricity generation mixes for 2040.

Flexible Charging loads of SAEVs enables substantial energy Savings

Meanwhile, SAEV charging loads are highly flexible and offer the potential for substantial cost savings of energy, which can range from 13% to 46% across simulated scenarios. If coordinated charging is used, the timing of charging events can be shifted to take advantage of lower electricity prices without having a negative impact on the fleet’s ability to satisfy the needs of users.

For the study, the team employed National Renewable Energy Laboratory’s Highly Integrated Vehicle Ecosystem simulation framework. The framework simulates the running of SAEV fleets over data sets of travel demand, along with local projected electricity rates from the Regional Energy Deployment System Model.

The flexible design of Highly Integrated Vehicle Ecosystem design enables researchers to construct large-scale simulation matrices and compare results across a number of possibilities that vary with respect to parameters such as location, charging and fueling station networks, dispatching algorithms, fleet operational behavior, and more.

Leave a Reply