Short‐Term Tests, Long‐Term Predictions – Accelerating Ageing Characterisation of Lithium‐Ion Batteries

期限(时间) 锂(药物) 老化 材料科学 离子 核工程 化学 物理 医学 工程类 内科学 有机化学 量子力学
作者
Sabine Paarmann,Markus Schreiber,Ahmed Chahbaz,Felix Hildenbrand,Gereon Stahl,Marcel Rogge,Philipp Dechent,Oliver Queisser,Sebastian Dominic Frankl,Pablo Morales Torricos,Yao Lu,Nikolay I. Nikolov,Maria Kateri,Dirk Uwe Sauer,Michael A. Danzer,Thomas Wetzel,Christian Endisch,Markus Lienkamp,Andreas Jossen,Meinert Lewerenz
出处
期刊:Batteries & supercaps [Wiley]
卷期号:7 (11) 被引量:22
标识
DOI:10.1002/batt.202300594
摘要

Abstract For the battery industry, quick determination of the ageing behaviour of lithium‐ion batteries is important both for the evaluation of existing designs as well as for R&D on future technologies. However, the target battery lifetime is 8–10 years, which implies low ageing rates that lead to an unacceptably long ageing test duration under real operation conditions. Therefore, ageing characterisation tests need to be accelerated to obtain ageing patterns in a period ranging from a few weeks to a few months. Known strategies, such as increasing the severity of stress factors, for example, temperature, current, and taking measurements with particularly high precision, need care in application to achieve meaningful results. We observe that this challenge does not receive enough attention in typical ageing studies. Therefore, this review introduces the definition and challenge of accelerated ageing along existing methods to accelerate the characterisation of battery ageing and lifetime modelling. We systematically discuss approaches along the existing literature. In this context, several test conditions and feasible acceleration strategies are highlighted, and the underlying modelling and statistical perspective is provided. This makes the review valuable for all who set up ageing tests, interpret ageing data, or rely on ageing data to predict battery lifetime.
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