A Large Language Model-Based Database for Analyzing the Electric Vehicle Battery Supply Chain

Global geopolitical volatility poses a critical threat to the resilience of the electric vehicle battery supply chain. Static, manually updated databases fail to capture the sector's rapid dynamics, creating significant information gaps for strategic planning. This study proposes an Artificial Intelligence-driven methodology to construct a comprehensive and dynamic knowledge base. An automated pipeline is implemented, beginning with specialized web crawlers that harvest real-time textual data from curated news and industry sources. This unstructured data is preprocessed through deduplication and cleansing to ensure quality. The core innovation involves applying fine-tuned Large Language Models (LLMs) to perform deep semantic parsing and extract structured information. These models precisely identify key entities—corporations, facilities, production capacities—and delineate the complex multi-tier relationships from raw material extraction to final distribution. The output is an interlinked knowledge graph that provides an unprecedented, data-rich representation of the global supply chain. It demonstrates the transformative potential of LLMs in industrial intelligence, delivering a critical tool for enhancing visibility, fostering resilience, and enabling data-driven decision-making for sustainable mobility amidst global disruptions.