系统梳理NLP在电池全生命周期的应用，提出新框架助力电池材料发现。
From the Rock Floor to the Cloud: A Systematic Survey of State-of-the-Art NLP in Battery Life Cycle
- 按PRISMA流程筛选274篇论文，精评66篇，覆盖电池全周期
- 发现新兴NLP任务推动材料发现，但缺乏统一基准
- 提出TLP框架融合智能体与优化提示，适配欧盟电池护照
我们系统性地综述了自然语言处理（NLP）在电池全生命周期中的应用，而非局限于单一阶段或方法，并为欧盟拟议的数字电池护照（DBP）及其他通用电池预测任务提出了一个新型技术语言处理（TLP）框架。研究遵循系统综述与元分析首选报告项目（PRISMA）规范，采用谷歌学术、IEEE Xplore和斯普林格·自然（Scopus）三大数据库进行检索，共评估274篇论文，最终筛选出66篇相关文献进行深度分析。研究公开提供评审数据集以确保可验证性和可复现性。结果显示，电池领域正涌现出新的NLP任务，有助于材料发现及其他生命周期阶段。然而，仍面临缺乏标准化基准等挑战。所提出的TLP框架结合了智能体人工智能与优化提示策略，有望应对部分难题。
原文摘要 · Abstract (English)
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.
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