Initial results show that demand during working hours is high in California
EMBARGO until Dec 10, 2024 1:30 pm (CST)
AUSTIN, TX, Dec 10, 2024 – Electric Vehicles (EVs) represent a promising mode of transportation that can help the United States reduce its carbon emissions. But barriers such as the high cost of installing and using EV Charging Stations (EVCS), their limitations in supplying emerging demand, and their uneven distribution throughout the country limit access for many Americans.
Researchers at the University of Maryland are using supercomputers and machine learning methods to analyze a full year of real-time data collected from individual EV charging ports at more than 50,000 publicly available stations throughout the country. The primary focus of the study is to estimate demand and peak times at EV charging stations.
“Understanding EV users’ charging behaviors will provide valuable insights into their needs, enabling us to efficiently deploy EV charging infrastructure to meet the emerging demand,” says civil engineering Ph.D. candidate Safoura Safari. She will present the team’s preliminary findings in December at the annual meeting of the Society for Risk Analysis in Austin, Texas.
Led by scientists in UMD’s Center for Global Sustainability and Center for Disaster Resilience, the study utilizes individual charging port statuses recorded every 10 minutes at 54,000 charging stations located in three power grid zones: California, Texas, and the Northeast. It is the first study of its kind to use real-time charging port data to understand charging behaviors across different time intervals.
Machine learning is used to extract “clusters” or groups of behavior from the data. In their analysis, the researchers look for variations of charging patterns on daily, monthly, and seasonal scales — accounting for temperature impacts on battery range (which impacts charging decisions).
The first round of analysis based on data for the month of August 2023, revealed working-hour patterns in all three zones, with differences in charging behavior and power demand:
Safari points out that the variation in charging behavior variation between zones may be due to several factors: differences in the price of electricity, the availability of public charging stations to EV owners, and incentive programs that may encourage owners to charge their vehicles during off-peak hours.
As they continue to crunch a year’s worth of data on supercomputers, the team expects to find other “clustered” patterns in EV charging station usage — for example, during overnight hours, holidays, local and national events, and weather-related disasters. They hope that power grid operators can use the results of their analysis to efficiently price electricity, invest in developing new charging stations or expand existing ones, and balance supply and demand — ensuring charging port availability and reducing wait times.
A secondary focus of the research is to explore potential inequities in access and use of EV charging stations on a national scale. “Our findings can set the foundation for future research on the equitable accessibility, availability, and utilization of EV charging facilities across diverse communities with varied socio-economic status,” says UMD assistant professor Jiehong Lou, assistant research director of the Center for Global Sustainability.
In a previously published study, Lou and colleagues found that lower-income households face less accessibility to public EV infrastructure in both urban and rural geographies.
According to a Pew Research Center report, there were over 61,000 publicly accessible charging stations in the U.S. as of February 2024. Experts have found that many more are needed to accommodate future EV ownership. EV charging stations are mostly accessible to residents in urban areas, with only 17 percent located in rural areas, according to the report.
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Safoura Safari, Deb Niemeier, Jiehong Lou are presenting this research on Tuesday, December 10, from 1:30 pm, at the JW Marriot Austin, Texas.
Spatial and Temporal Patterns of EV Charging Usage: A Nationwide Analysis Using Machine Learning Techniques – Tuesday, December 10, 1:30 p.m.
About SRA
The Society for Risk Analysis is a multidisciplinary, interdisciplinary, scholarly, international society that provides an open forum for all those interested in risk analysis. SRA was established in 1980. Since 1982, it has continuously published Risk Analysis: An International Journal, the leading scholarly journal in the field. For more information, visit www.sra.org.
