Stroebl Florian, Petersohn Ronny, Schricker Barbara, Schaeufl Florian, Bohlen Oliver, Palm Herbert
Institute for Sustainable Energy Systems (ISES), Munich University of Applied Sciences, Munich, 80335, Germany.
Hoppecke Systemtechnik GmbH, Advance Development, Zwickau, 08056, Germany.
Sci Data. 2024 Sep 19;11(1):1020. doi: 10.1038/s41597-024-03859-z.
This dataset encompasses a comprehensive investigation of combined calendar and cycle aging in commercially available lithium-ion battery cells (Samsung INR21700-50E). A total of 279 cells were subjected to 71 distinct aging conditions across two stages. Stage 1 is based on a non-model-based design of experiments (DoE), including full-factorial and Latin hypercube experimental designs, to determine the degradation behavior. Stage 2 employed model-based parameter individual optimal experimental design (pi-OED) to refine specific dependencies, along with a second non-model-based approach for fair comparison of DoE methodologies. While the primary aim was to validate the benefits of optimal experimental design in lithium-ion battery aging studies, this dataset offers extensive utility for various applications. They include training of machine learning models for battery life prediction, calibrating of physics-based or (semi-)empirical models for battery performance and degradation, and numerous other investigations in battery research. Additionally, the dataset has the potential to uncover hidden dependencies and correlations in battery aging mechanisms that were not evident in previous studies, which often relied on pre-existing assumptions and limited experimental designs.
该数据集涵盖了对市售锂离子电池（三星INR21700 - 50E）日历老化和循环老化相结合的全面研究。总共279个电池在两个阶段经历了71种不同的老化条件。第一阶段基于非模型的实验设计（DoE），包括全因子实验设计和拉丁超立方实验设计，以确定降解行为。第二阶段采用基于模型的参数个体最优实验设计（pi - OED）来优化特定的相关性，同时采用第二种非模型方法来公平比较DoE方法。虽然主要目的是验证最优实验设计在锂离子电池老化研究中的益处，但该数据集在各种应用中具有广泛的用途。它们包括用于电池寿命预测的机器学习模型训练、用于电池性能和降解的基于物理或（半）经验模型的校准，以及电池研究中的许多其他调查。此外，该数据集有可能揭示电池老化机制中隐藏的依赖性和相关性，而这些在以前的研究中并不明显，以前的研究往往依赖于预先存在的假设和有限的实验设计。
