Georgia Tech Team Uses Machine Learning to Drive Electric Vehicle Policy Findings
By Michael Pearson
With electric vehicles poised to hit the mainstream, building out the nationwide network of charging stations to keep them going will be increasingly important.
A new study from the Georgia Institute of Technology School of Public Policy harnesses machine learning techniques to provide the best insight yet into the attitudes of electric vehicle (EV) drivers towards the existing charger network. The study findings could help policymakers focus their efforts.
In the paper, which is featured on the cover of the June 2020 issue of Nature Sustainability, a team led by Assistant Professor Omar Isaac Asensio trained a machine learning algorithm to analyze unstructured consumer data from 12,270 electric vehicle charging stations across the United States.
The study demonstrates how machine learning tools can be used to quickly analyze streaming data for policy evaluation in near real-time (see sidebar). Streaming data refers to data that comes in a feed, continuously, such as user reviews from an app. The study also revealed surprising findings about how EV drivers feel about charging stations.
For instance, it turns out that the conventional wisdom that drivers prefer private stations to public ones appears to be wrong. The study also finds potential problems with charging stations in the bigger cities, presaging challenges yet to come in creating a robust charging system that meets drivers' needs.
“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 wrote.