Uncovering latent relationships among EV battery characteristics in EV battery products using topic modelling

Abstract

This study explores the application of Topic Modeling using Latent Dirichlet Allocation in uncovering latent relationships in Electric Vehicle batteries. This research aims to provide valuable insights into the key characteristics, advancements, and challenges in the field. The methodology involves preprocessing the text data, training the LDA model, and analyzing the resulting topics. Key findings shed light on critical aspects such as battery chemistry, performance metrics, charging infrastructure, environmental impact, and future advancements. These insights contribute to a more comprehensive understanding of EV battery technology, aiding informed decision-making and fostering innovation and sustainability in the transportation sector. By leveraging Topic Modeling, this study contributes to a deeper understanding of EV battery technology, facilitating informed decision-making, innovation, and sustainability in the transportation sector.