SULI Oral Presentation

Clifford, Jason Patrick

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DOE OSTI · 1994197

SULI Oral Presentation

Clifford, Jason Patrick

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Abstract

Furthering our understanding of the prevalence and severity of issues that customers face when charging their electric vehicles (EVs) is crucial in order to improve the charging experience across the United States. This project utilizes web-scraping, machine leaning (ML), and natural language processing (NLP) techniques to analyze and categorize user-generated reviews. Selenium was used to build a data collection tool that can scrape vast amounts of user review data from the PlugShare website. Sentiment analysis was employed on this dataset in order to filter out negative reviews for further analysis. NLP techniques such as tokenization and word embedding were then used to convert user-written comments into a numerical format that a ML model can interpret. Multiple ML approaches are currently being explored in order to identify and categorize the charging issues being talked about in each review. Ultimately, the results from the ML model will be visualized and explained in a report on customer pain points to be delivered to the ChargeX Consortium, therefore revealing specific areas for improvement in the customer charging experience.

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29 ENERGY PLANNING, POLICY, AND ECONOMY Electric Vehicle Infrastructure Vehicle Grid Integration

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Clifford, Jason Patrick. 2023-08-02. SULI Oral Presentation. https://www.osti.gov/biblio/1994197

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