Multi-Stage Lithium Ion Battery Aging Study

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.

本数据集针对市售锂离子电池单体（Samsung INR21700-50E）系统性开展了日历老化与循环老化的耦合效应研究。总计279颗电池单体历经两个测试阶段，覆盖71种差异化老化工况。第一阶段采用非模型驱动实验设计（Design of Experiments, DoE），包含全因子实验设计与拉丁超立方实验设计，以探明电池的退化行为规律。第二阶段则通过基于模型的参数个体最优实验设计（parameter individual Optimal Experimental Design, pi-OED）来细化特定的依赖关系，同时辅以第二种非模型驱动实验方案，以公平对比各类实验设计方法的优劣。尽管本数据集的核心目标是验证最优实验设计在锂离子电池老化研究中的应用优势，但其同时具备广泛的多场景应用潜力。具体应用方向包括：训练用于电池寿命预测的机器学习模型、校准用于电池性能与退化分析的物理驱动或（半）经验模型，以及开展电池研究领域的各类其他相关探究。此外，相较于过往依赖先验假设与有限实验设计的研究，本数据集还有助于挖掘电池老化机制中此前未被发现的潜在依赖关系与关联特性。

搜集汇总

背景与挑战

背景概述

该数据集包含279个商用锂离子电池在71种老化条件下的多阶段老化研究数据，采用不同实验设计方法(非模型基础和模型基础)进行测试，适用于电池寿命预测、性能模型校准等研究领域。数据集由德国联邦经济事务和能源部资助，采用CC BY 4.0许可协议。