Researchers use machine learning to analyze attitudes toward electric vehicle charging, in SLA GC-funded study

June 9, 2020

A study published in Nature Sustainability offers new insights into the attitudes of electric vehicle (EV) drivers about the existing charger network. The research team, led by Omar Issac Asensio (assistant professor at the Georgia Institute of Technology School of Public Policy), trained a machine learning algorithm to analyze consumer data from 12,270 electric vehicle charging stations in the United States. 

Consumers were equally satisfied with government-operated public charging stations and private charging stations, the study found. It also identified potential problems with charging stations in larger cities. The findings of the study may be used to determine how well EV infrastructure is meeting consumer needs and help to inform policies. 

Photo Source: Unsplash

“Based on evidence from consumer data, we argue that it is not enough to just invest money into increasing the quantity of stations, it is also important to invest in the quality of the charging experience,” Asensio writes. 

The research was funded, in part, by the National Science Foundation and Sustainable LA Grand Challenge while Asensio was a postdoctoral scholar with the UCLA Anderson School of Management Ziman Center for Real Estate.

Explore related research on the Research Portal

Additional coverage:

What Do Electric Vehicle Drivers Think of the Charging Network They Use?. Georgia Tech News Center. June 9, 2020. 

Georgia Tech Team Uses Machine Learning to Drive Electric Vehicle Policy Findings. Georgia Tech News Center. June 9, 2020. 

VIDEO: Georgia Tech School of Public Policy Electric Vehicle Charging Study. Ivan Allen College of Liberal Arts. June 4, 2020