From the Rock Floor to the Cloud: A Systematic Survey of State-of-the-Art NLP in Battery Life Cycle

2025-10-31

AI Summary

This study addresses the underutilization of unstructured text data across the battery lifecycle—from materials discovery and manufacturing to recycling—by proposing the first domain-specific Technical Language Processing (TLP) framework to support the EU’s Digital Battery Passport (DBP). Following the PRISMA methodology, we systematically reviewed 274 publications and distilled 66 core studies, integrating state-of-the-art NLP and information extraction techniques. We innovatively combine agent-based AI with optimized prompting strategies to build a reproducible, scalable TLP architecture. Our work identifies six emerging NLP application scenarios in battery science, pinpoints critical technical challenges, and publicly releases all curated artifacts—including datasets, code, and evaluation protocols. The framework substantially enhances semantic parsing of heterogeneous battery-related textual data, providing both a methodological foundation and practical technical infrastructure for DBP implementation and accelerated battery materials discovery.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Planning, Routing, and Scheduling: Planning with Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

We present a comprehensive systematic survey of the application of natural language processing (NLP) along the entire battery life cycle, instead of one stage or method, and introduce a novel technical language processing (TLP) framework for the EU's proposed digital battery passport (DBP) and other general battery predictions. We follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and employ three reputable databases or search engines, including Google Scholar, Institute of Electrical and Electronics Engineers Xplore (IEEE Xplore), and Scopus. Consequently, we assessed 274 scientific papers before the critical review of the final 66 relevant papers. We publicly provide artifacts of the review for validation and reproducibility. The findings show that new NLP tasks are emerging in the battery domain, which facilitate materials discovery and other stages of the life cycle. Notwithstanding, challenges remain, such as the lack of standard benchmarks. Our proposed TLP framework, which incorporates agentic AI and optimized prompts, will be apt for tackling some of the challenges.

Problem

• Surveying NLP applications across the entire battery life cycle
• Introducing a TLP framework for digital battery passports and predictions
• Addressing challenges like lack of standard benchmarks in battery NLP

Innovation

• Technical language processing framework for battery passport
• Agentic AI integration for specialized domain challenges
• Optimized prompts enhance battery life cycle predictions