• HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
Friday, March 5, 2021
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
  • HOME
  • NEWS
    • BIOENGINEERING
    • SCIENCE NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • FORUM
    • INSTAGRAM
    • TWITTER
  • CONTACT US
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News Chemistry

AI trained to read electric vehicle charging station reviews to find infrastructure gaps

Bioengineer by Bioengineer
January 22, 2021
in Chemistry
0
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

IMAGE

Credit: Ha et al./Patterns

Although electric vehicles that reduce greenhouse gas emissions attract many drivers, the lack of confidence in charging services deters others. Building a reliable network of charging stations is difficult in part because it’s challenging to aggregate data from independent station operators. But now, researchers reporting January 22 in the journal Patterns have developed an AI that can analyze user reviews of these stations, allowing it to accurately identify places where there are insufficient or out-of-service stations.

“We’re spending billions of both public and private dollars on electric vehicle infrastructure,” says Omar Asensio (@AsensioResearch), principal investigator and assistant professor in the School of Public Policy at the Georgia Institute of Technology. “But we really don’t have a good understanding of how well these investments are serving the public and public interest.”

Electric vehicle drivers have started to solve the problem of uncertain charging infrastructure by forming communities on charge station locator apps, leaving reviews. The researchers sought to analyze these reviews to better understand the problems facing users.

With the aid of their AI, Asensio and colleagues were able to predict whether a specific station was functional on a particular day. They also found that micropolitan areas, where the population is between 10,000 and 50,000 people, may be underserved, with more frequent reports of station availability issues. These communities are mostly located in states in the West and Midwest, such as Oregon, Utah, South Dakota, and Nebraska, along with Hawaii.

“When users are engaging and sharing information about charging experiences, they are often engaging in prosocial or pro-environmental behavior, which gives us rich behavioral information for machine learning,” says Asensio. But compared to analyzing data tables, texts can be challenging for computers to process. “A review could be as short as three words. It could also be as long as 25 or 30 words with misspellings and multiple topics,” says co-author Sameer Dharur of Georgia Institute of Technology. Users sometimes even throw smiley faces or emojis into the texts.

To address the problem, Asensio and his team tailored their algorithm to electric vehicle transportation lingo. They trained it with reviews from 12,720 US charging stations to classify reviews into eight different categories: functionality, availability, cost, location, dealership, user interaction, service time, and range anxiety. The AI achieved a 91% accuracy and high learning efficiency in parsing the reviews in minutes. “That’s a milestone in the transition for us to deploy these AI tools because it’s no longer ‘can the AI do as good as human?'” says Asensio. “In some cases, the AI exceeded the performance of human experts.”

As opposed to previous charging infrastructure performance evaluation studies that rely on costly and infrequent self-reported surveys, AI can reduce research costs while providing real-time standardized data. The electric vehicle charging market is expected to grow to $27.6 billion by 2027. The new method can give insight into consumers’ behavior, enabling rapid policy analysis and making infrastructure management easier for the government and companies. For instance, the team’s findings suggest that it may be more effective to subsidize infrastructure development as opposed to the sale of an electric car.

While the technology still faces some limitations–like the need to reduce requirements for computer processing power–before rolling out large-scale implementation to the electric vehicle charging market, Asensio and his team hope that as the science progresses, their research can open doors to more in-depth studies about social equity on top of meeting consumer needs.

“This is a wake-up call for us because, given the massive investment in electric vehicle infrastructure, we’re doing it in a way that is not necessarily attentive to the social equity and distributional issues of access to this enabling infrastructure,” says Asensio. “That is a topic of discussion that’s not going away and we’re only beginning to understand.”

###

This work was supported by the National Science Foundation, Microsoft Azure Sponsorship, and the Ivan Allen College Dean’s SGR-C Award.

Patterns, Ha et al.: “Topic Classification of Electric Vehicle Consumer Experiences with Transformer-Based Deep Learning”
https://www.cell.com/patterns/fulltext/S2666-3899(20)30265-8

Patterns (@Patterns_CP), published by Cell Press, is a data science journal publishing original research focusing on solutions to the cross-disciplinary problems that all researchers face when dealing with data, as well as articles about datasets, software code, algorithms, infrastructures, etc., with permanent links to these research outputs. Visit: https://www.cell.com/patterns. To receive Cell Press media alerts, please contact [email protected]

Media Contact
Carly Britton
[email protected]

Related Journal Article

http://dx.doi.org/10.1016/j.patter.2020.100195

Tags: Civil EngineeringClimate ChangeEnergy/Fuel (non-petroleum)Robotry/Artificial IntelligenceTechnology/Engineering/Computer ScienceVehicles
Share12Tweet8Share2ShareShareShare2

Related Posts

IMAGE

Study shows cactus pear as drought-tolerant crop for sustainable fuel and food

March 5, 2021
IMAGE

Christopher Tunnell wins NSF CAREER Award

March 5, 2021

Tantalizing signs of phase-change ‘turbulence’ in RHIC collisions

March 5, 2021

Species are our livelihoods

March 5, 2021

Leave a Reply Cancel reply

Your email address will not be published.

This site uses Akismet to reduce spam. Learn how your comment data is processed.

POPULAR NEWS

  • IMAGE

    Terahertz accelerates beyond 5G towards 6G

    667 shares
    Share 267 Tweet 167
  • People living with HIV face premature heart disease and barriers to care

    84 shares
    Share 34 Tweet 21
  • Global analysis suggests COVID-19 is seasonal

    38 shares
    Share 15 Tweet 10
  • HIV: an innovative therapeutic breakthrough to optimize the immune system

    36 shares
    Share 14 Tweet 9

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Tags

GeneticsInfectious/Emerging DiseasesTechnology/Engineering/Computer ScienceClimate ChangeChemistry/Physics/Materials SciencesPublic HealthMedicine/HealthBiologyMaterialsCell BiologyEcology/Environmentcancer

Recent Posts

  • New ‘split-drive’ system puts scientists in the (gene) driver seat
  • Online dating: Super effective, or just… superficial?
  • UTA engineer aims to reduce natural disaster damage to transportation infrastructure
  • New molecular driver of frontal circuit maturation discovered
  • Contact Us

© 2019 Bioengineer.org - Biotechnology news by Science Magazine - Scienmag.

No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

© 2019 Bioengineer.org - Biotechnology news by Science Magazine - Scienmag.

Welcome Back!

Login to your account below

Forgotten Password?

Create New Account!

Fill the forms below to register

All fields are required. Log In

Retrieve your password

Please enter your username or email address to reset your password.

Log In