Classifying Invention Objectives of Electric Vehicle Chargers through Natural Language Processing and Machine Learning

Abstract

The gradual adoption of electric vehicles (EVs) globally serves as a crucial move toward addressing global decarbonization goals for sustainable development. However, the lack of cost-effective, power-efficient, and safe chargers for EV batteries hampers adoption. Understanding the research needs and identifying the gaps in EV charger innovation informs investments and research to address development challenges. This study developed a unique text mining workflow to classify themes in EV charger technology and product development by analyzing U.S. patent award summaries. The text mining workflow combined the techniques of data extraction, data cleaning, natural language processing (NLP), statistical analysis, and unsupervised machine learning (ML) to extract unique themes and to visualize their relationships. There was a 47.7% increase in the number of EV charger patents issued in 2022 relative to that in 2018. The top four themes were charging station management, power transfer efficiency, on-board charger design, and temperature management. More than half (53.8%) of the EV charger patents issued over the five-year period from 2018 to 2022 addressed problems within those four themes. Patents that addressed wireless charging, fast charging, and fleet charging accounted for less than 10% each of the EV charger patents issued. This suggests that the industry is still at the frontier of addressing those problems. This study further presents examples of the specific EV charger problems addressed within each theme. The findings can inform investment decisions and policymaking to focus on R&D resources that will advance the state of the art and spur EV adoption.