As electric vehicles (EVs) go mainstream, building out the nationwide network of charging stations to keep them going will be increasingly important. The new findings could help policymakers focus their efforts.

In the paper, published in the journal Nature Sustainability, describes training a machine learning algorithm to analyze unstructured consumer data from 12,270 electric vehicle charging stations across the US.

The study demonstrates how machine learning tools can help quickly analyze streaming data for policy evaluation in near-real time. Streaming data refers to data that comes in a continuous feed, such as user reviews from an app. The study also reveals surprising findings about how EV drivers feel about charging stations.

For instance, 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 larger cities, presaging challenges yet to come in creating a robust charging system that meets all 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,” writes assistant professor Omar Isaac Asensio of the Georgia Institute of Technology School of Public Policy who led the team.

Electric vehicles are considered a crucial part of the solution to climate change: transportation is now the leading contributor of climate-warming emissions. But one major barrier to broader adoption of electric vehicles is the perception of a lack of charging stations, and the attending “range anxiety” that makes many drivers nervous about buying an EV.

What do drivers want?

While that infrastructure has grown considerably in recent years, the work hasn’t taken into account what consumers actually want, Asensio says.

“In the early years of EV infrastructure development, most policies were geared to using incentives to increase the quantity of charging stations,” Asensio says. “We haven’t had enough focus on building out reliable infrastructure that can give confidence to users.”

This study helps rectify that shortcoming by offering evidence-based, national analysis of actual consumer sentiment, as opposed to indirect travel surveys or simulated data used in many analyses.

Asensio’s team used deep learning text classification algorithms to analyze data from a popular EV users smartphone app. It would have taken most of a year using conventional methods. But the team’s approach cut the task down to minutes while classifying sentiment with accuracy similar to that of human experts.

Favorite electric vehicle charging stations

The study found that workplace and mixed-use residential stations get low ratings, with frequent complaints about lack of accessibility and signage. Fee-based charging stations tend to get more poor reviews than free charging stations. But it is stations in dense urban centers that really draw complaints, according to the study.

When researchers controlled for location and other characteristics, stations in dense urban areas showed a 12-15% increase in negative sentiment compared to nonurban locations.

This could indicate a broad range of service quality issues in the largest EV markets, including things like malfunctioning equipment and an insufficient number of chargers, Asensio says.

The highest rated stations are often located at hotels, restaurants, and convenience stores, a finding that may support incentive-based management practices in which chargers are installed to draw customers. Stations at public parks and recreation facilities, RV parks, and visitor centers also do well, according to the study.

But, contrary to theories predicting that private stations should provide more efficient services, the study found no statistically significant difference in user preferences when it comes to public versus private chargers.

That finding could be an inducement to invest in public charging infrastructure to meet future growth, Asensio says. A study by the National Research Council cited such a network as key to helping overcome barriers to EV adoption.

Consumer data, faster

Overall, Asensio says the study points to the need to prioritize consumer data when considering how to build out infrastructure, especially when it comes to requirements for charging stations in new buildings.

But EV policy is not the only way the study’s deep learning techniques can be used to analyze this kind of material. They could be adapted to a broad range of energy and transportation issues, allowing researchers to deliver rapid analysis with just minutes of computation, compared to time lags measured sometimes in months or years using more traditional methods.

“The follow-on potential for energy policy is to move toward automated forms of infrastructure management powered by machine learning, particularly for critical linkages between energy and transportation systems and smart cities,” Asensio says.