Electric Vehicle Widescale Analysis for Tomorrow’s Transportation Solutions
Awarded Date: September 2019
Completion Date: December 2022
EV WATTS is responding to the rapid increase in vehicle electrification by collecting, analyzing, and summarizing up-to-date national data from plug-in electric vehicles (PEVs) and charging stations (also known as EVSE – electric vehicle supply equipment) in order to improve the understanding of end user charging and driving patterns as well as future development and deployment. Energetics is partnering with Clean Cities Coalitions, state and local governments, fleets, and charging station providers to collect and analyze non-identifiable data over an 18-24 month period.
“EV WATTS will facilitate unbiased analyses, drawing on diverse data that provides robust results from a broad stakeholder group. Having access to a uniform, comprehensive, and anonymized dataset from recent PEV and EVSE deployments across the U.S. will help Argonne National Laboratory understand charging behavior by vehicle class and trip purpose. It will also shed light on the correlation between EVSE availability and PEV adoption, laying the foundation for better future placements of infrastructure.”
— Joann Zhou, Mobility and Deployment Group Leader, Argonne National Laboratory
Goals and Benefits:
- Provide anonymized (non-identifiable information) PEV and EVSE data that augments existing DOE federally funded and development datasets
- Develop and regularly share high-level data summaries that provide stakeholders and the public with a snapshot of current PEV and EVSE operations and trends
- Apply data analytics to answer the project’s key research questions that will inform the next generation of policies and investments
- Include all-electric and plug-in hybrid electric vehicles ranging from light, medium, and heavy duty
- Include various sites from corridors, workplaces, curbsides, fleets, commercial, etc.
- Data sharing partners have rights to data from PEV charging stations and/or PEVs equipped with telematics data loggers
- All personally identifiable information is removed and the data entries are anonymized