Аналіз відкритих наборів експериментальних даних літій-іонних батарей для моделювання деградації та управління життєвим циклом

• Datum: 2026
• Hauptverfasser: Kostenko , Ganna, Bosak, Andrii, Sapsai, Yurii, Zaporozhets, Artur
• Format: Artikel
• Sprache: Englisch
• Veröffentlicht: General Energy Institute of the National Academy of Sciences of Ukraine 2026
• Schlagworte: літій-іонні батареї; відкриті набори даних; управління життєвим циклом; моделювання деградації; система KPI; повторне використання батарей; енергетичні системи на основі даних.
• Online Zugang: https://systemre.org/index.php/journal/article/view/938
• Назва журналу: System Research in Energy

The rapid expansion of battery-powered mobility and renewable energy integration has amplified the demand for energy storage as well as for reliable and standardized data supporting predictive modeling and lifecycle management of lithium-ion batteries. Experimental testing of batteries is costly, time-consuming, and limited by laboratory constraints, which makes open-access datasets an invaluable foundation for comparative studies, model validation, and reproducible analytics. However, the diversity of available datasets in terms of format, chemistry, and test conditions complicates their systematic use in degradation and KPI-based research. This study develops an information and analytical framework for the selection, evaluation, and classification of open-access Li-ion battery datasets applicable to both first- and second-life applications. Fifteen representative datasets were analyzed and grouped by chemistry, cycling depth, and metadata completeness, with additional assessment of data integrity and traceability according to FAIR principles. The analysis identifies dataset suitability for specific analytical domains: Sandia and HNEI provide long-term degradation and RUL modeling data; Stanford SLB and UC Davis Microgrid datasets enable operational and KPI analysis under realistic usage conditions; PulseBat and Panasonic PF datasets contribute to safety, reliability, and probabilistic risk evaluation. The proposed framework establishes clear connections between raw data, degradation indicators, and system-level metrics such as LCOS, utilization rate, and lifecycle efficiency. It also introduces a structured mapping of data relevance to various modeling objectives, supporting reproducible and cross-compatible research across laboratories and applications. Beyond comparative analysis, the study emphasizes the critical role of metadata completeness, DOI-based traceability, and repository-level version control in building trustworthy digital twins and regulatory tools such as the EU Battery Passport. The results provide a foundation for harmonized, data-driven methodologies that bridge experimental data, predictive models, and KPI-based lifecycle management, promoting transparency, interoperability, and sustainability in battery research and deployment. Additionally, a detailed comparative analysis of three representative high-quality datasets (Sandia NMC, HNEI LFP, and NASA/CALCE NMC/NCA) is presented to illustrate chemistry-dependent degradation behaviour and quantify inter-dataset divergence relevant for second-life modeling.