Batteries are pivotal for transitioning to a climate-friendly future, leading to a surge in battery research. Scopus (Elsevier) lists 14,388 papers that mention "lithium-ion battery" in 2023 alone, making it infeasible for individuals to keep up. This paper discusses strategies based on structured, semantic, and linked data to manage this information overload. Structured data follows a predefined, machine-readable format; semantic data includes metadata for context; linked data references other semantic data, forming a web of interconnected information. We use a battery-related ontology, BattINFO to standardise terms and enable automated data extraction and analysis. Our methodology integrates full-text search and machine-readable data, enhancing data retrieval and battery testing. We aim to unify commercial cell information and develop tools for the battery community such as manufacturer-independent cycling procedure descriptions and external memory for Large Language Models. Although only a first step, this approach significantly accelerates battery research and digitalizes battery testing, inviting community participation for continuous improvement. We provide the structured data and the tools to access them as open source.

翻译：电池对于向气候友好型未来转型至关重要，这推动了电池研究的热潮。仅2023年，Scopus（Elsevier）就收录了14,388篇提及“锂离子电池”的论文，使得个人难以全面跟进。本文探讨了基于结构化、语义化和关联数据的策略，以应对这种信息过载问题。结构化数据遵循预定义的机器可读格式；语义数据包含用于提供上下文的元数据；关联数据则引用其他语义数据，从而形成一个相互连接的信息网络。我们使用电池相关本体BattINFO来标准化术语，并实现自动化数据提取与分析。我们的方法整合了全文搜索和机器可读数据，从而增强了数据检索和电池测试能力。我们的目标是统一商用电池信息，并为电池社区开发工具，例如制造商无关的循环程序描述以及用于大型语言模型的外部记忆。尽管这仅是第一步，但该方法显著加速了电池研究并实现了电池测试的数字化，我们诚邀社区参与以持续改进。我们将结构化数据及访问工具作为开源资源提供。