A multi-stage lithium-ion battery aging dataset using various experimental design methodologies.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-09-19 DOI:10.1038/s41597-024-03859-z

Florian Stroebl, Ronny Petersohn, Barbara Schricker, Florian Schaeufl, Oliver Bohlen, Herbert Palm

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Abstract

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.

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使用各种实验设计方法的多阶段锂离子电池老化数据集。

该数据集包括对市售锂离子电池（三星 INR21700-50E）的日历老化和循环老化的综合调查。共有 279 节电池在 71 种不同的老化条件下经历了两个阶段。第一阶段基于非模型实验设计（DoE），包括全因子和拉丁超立方实验设计，以确定降解行为。第 2 阶段采用基于模型的参数个体优化实验设计（pi-OED）来完善特定的依赖关系，同时采用第二种非基于模型的方法对 DoE 方法进行公平比较。虽然主要目的是验证优化实验设计在锂离子电池老化研究中的益处，但该数据集也为各种应用提供了广泛的实用性。这些应用包括训练用于电池寿命预测的机器学习模型、校准基于物理或（半）经验的电池性能和退化模型，以及电池研究中的许多其他调查。此外，该数据集还有可能发现电池老化机制中隐藏的依赖性和相关性，而这些在以往的研究中并不明显，因为以往的研究往往依赖于已有的假设和有限的实验设计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.