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.
翻译：本文对自然语言处理（NLP）在电池全生命周期中的应用进行了全面系统的综述，而非局限于单一阶段或方法，并针对欧盟提出的数字电池护照（DBP）及其他通用电池预测任务，引入了一种新颖的技术语言处理（TLP）框架。我们遵循系统综述与荟萃分析优先报告条目（PRISMA）方法，采用三个权威数据库或搜索引擎，包括Google Scholar、电气电子工程师学会Xplore（IEEE Xplore）和Scopus。最终，在严格评审后从274篇科学文献中筛选出66篇相关论文进行深入分析。我们公开提供本次综述的研究材料以确保可验证性与可复现性。研究结果表明，电池领域正涌现出新的NLP任务，这些任务促进了材料发现及生命周期其他阶段的发展。然而，该领域仍面临缺乏标准基准等挑战。我们提出的TLP框架融合了智能体人工智能与优化提示技术，将有望应对部分挑战。
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