Tracking the dynamic evolution of lithium-ion battery recycling technology using natural language processing.

Area of Science:

• Materials Science and Engineering
• Environmental Science
• Information Science

Background:

• Spent lithium-ion batteries (LIBs) pose significant environmental and resource challenges.
• Existing research on LIB recycling technology lacks a comprehensive analysis of its evolutionary trajectory and future directions.
• A systematic approach is needed to understand the dynamic development of LIB recycling technologies.

Purpose of the Study:

• To propose a novel framework for analyzing the evolution of LIB recycling technology.
• To identify key technological topics and their development trends over time.
• To provide insights into future research and development (R&D) directions for LIB recycling.

Main Methods:

• Utilized change point detection to quantitatively segment LIB recycling technology development periods.
• Employed Latent Dirichlet Allocation (LDA) to identify technical topics within each period.
• Applied Doc2vec and cosine similarity to construct technology evolution paths.
• Defined a two-dimensional evaluation model to pinpoint future R&D opportunities.

Main Results:

• Successfully traced the dynamic evolution of LIB recycling technology topics.
• Identified distinct phases and shifts in technological focus within LIB recycling.
• The framework effectively highlights emerging and declining research areas.
• The two-dimensional model provides a clear roadmap for future LIB recycling R&D.

Conclusions:

• The proposed framework offers a systematic method for analyzing technology evolution in the LIB recycling sector.
• This study enhances the understanding of the dynamic landscape of LIB recycling.
• The framework is adaptable for analyzing technological evolution in other scientific fields.
• Provides valuable guidance for R&D investment and strategic planning in battery recycling.