基于大语言模型RAG架构的电池加速研究:现状与展望

Accelerating battery research with retrieval-augmented large language models:Present and future

随着近年电池领域研究投入的激增,研究人员面临着前所未有的信息过载和知识盲区的挑战。针对这一问题,本文探讨了大语言模型(large language model,LLM)的检索增强生成(retrieval augmented generation,RAG)架构在电池领域的应用潜力,在此基础上对近期的研究文献进行综述,并提出展望。本文介绍了大语言模型RAG架构的工作原理,强调了该架构在垂直领域的可靠性,并基于此综述探讨了该架构在电池材料设计、电池单元设计和制造、电动交通与电网的电池管理系统三个领域的潜在应用。在电池材料设计部分,本文着重分析了大语言模型RAG架构的无幻觉生成能力在数据提取、研究方案设计和多模态数据问答中的优势。在电池单元设计和制造部分,本文从科研端指出该架构对电池单元设计方案分析的辅助作用,从制造端指出该架构桥接产业和科研的鸿沟、辅助产业管控的作用。在电动交通和电网的电池管理系统部分,本文指出该架构在跨领域知识联结、辅助系统级运维的作用。最后,本文讨论了多模态RAG技术在电池研究领域的应用潜力及其对电池研究效率的提升,并展望了RAG在电池领域的更多应用前景。

In recent years,the surge in research investment within the battery field has presented researchers with challenges of information overload and knowledge gaps.This study examines the Retrieval-Augmented Generation(RAG)architecture of large language models in the battery domain,offering a review of contemporary research and future prospects.We describe the working principles of the RAG architecture,affirm its reliability in specialized domains,and discuss its applications across three key areas as follows:battery material design,battery cell design and manufacturing,and battery management systems for e-mobility and electric grids.In the section on battery material design,the study highlights the hallucination-free generation capabilities of RAG in data extraction,research protocol design,and multimodal data querying.The section on battery cell design and manufacturing elucidates RAG's role in enhancing research-driven battery cell design and bridging the gap between industry and academia,thereby aiding industrial control processes.The discussion on battery management systems for e-mobility and electric grids underscores RAG's contribution to cross-domain knowledge integration and system-level operation and maintenance support.The study concludes by considering the application of multimodal RAG technology in battery research and anticipates further expansion of RAG applications in this field.