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An ontology for the structured storage, retrieval, and analysis of data on lithium-ion battery materials and electrode-to-cell production is presented. It provides a logical structure that is mapped onto a digital architecture and used to visualize, correlate, and make predictions in battery production, research, and development. Materials and processes are specified using a predetermined terminology; a chain of unit processes (steps) connects raw materials and products (items) of battery cell production. The ontology enables the attachment of analytical methods (characterization methods) to items. Workshops and interviews with experts in battery materials and production processes are conducted to ensure that the structure is conformable both for industrial-scale and laboratory-scale data generation and implementation. Raw materials and intermediate products are identified and defined for all steps to the final battery cell. Steps and items are defined based on current standard materials and process chains using terms that are in common use. Alternative structures and the connection of the ontology to other existing ontologies are discussed. The contribution provides a pragmatic, accessible way to unify the storage of materials-oriented lithium-ion battery production data. It aids the linkage of such data with domain knowledge and the automation of data analysis in production and research.
The significant demand for energy storage systems has spurred innovative designs and extensive research on lithium-ion batteries (LIBs). To that end, an in-depth examination of utilized materials and relevant methods in conjunction with comparing electrochemical mechanisms is required. Lithium titanate (LTO) anode materials have received substantial interest in high-performance LIBs for numerous applications. Nevertheless, LTO is limited due to capacity fading at high rates, especially in the extended potential range of 0.01–3.00 V versus Li+/Li, while delivering the theoretical capacity of 293 mAh g−1. This study demonstrates how the performance of the LTO anode can be improved by modifying the manufacturing process. Altering the dry and wet mixing duration and speeds throughout the manufacturing process leads to differences in particle sizes and homogeneity of dispersion and structure. The optimized anode at 5 A g−1 (≈17C) and 10 A g−1 (≈34C) yielded 188 and 153 mAh g−1 and retained 73% and 68% of their initial capacity after 1000 cycles, respectively. The following findings offer valuable information regarding the empirical modifications required during electrode fabrication. Additionally, it sheds light on the potential to produce efficient anodes using commercial LTO powder.
Materials science research faces challenges due to diverse and evolving measurements, materials, and methods. Managing research data in a way that is understandable, comparable, and reproducible is essential for high data quality, particularly for data science and machine learning applications. In Li-ion batteries research data storage concepts and structures vary widely between institutions and researchers, leading to difficulties in data comparison and understanding. To address the issue of data structuring, battery production and characterization ontology (BPCO) is developed. The ontology builds on existing ontologies like the Platform MaterialDigital core ontology and quantities, units, dimensions, and types ontology to model standard battery production processes, characterization methods, and materials. The BPCO is based on a workflow structure to be accessible to nonexperts and, unlike highly specialized existing ontologies, models the whole production process removing the need for separate data structures and enabling the identification of dependencies between parameters. This work builds upon a previously published paper in which the taxonomy and fundamental strategies for ontology development are established. The article presents the developed ontology and its use for structuring research data in three key use cases, that is, different experiments performed to validate the ontology's capabilities, provide feedback, and ensure its applicability.
