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A decade of insights: Delving into calendar aging trends and implications
Joule ( IF 38.6 ) Pub Date : 2024-12-18 , DOI: 10.1016/j.joule.2024.11.013 Vivek N. Lam, Xiaofan Cui, Florian Stroebl, Maitri Uppaluri, Simona Onori, William C. Chueh
Joule ( IF 38.6 ) Pub Date : 2024-12-18 , DOI: 10.1016/j.joule.2024.11.013 Vivek N. Lam, Xiaofan Cui, Florian Stroebl, Maitri Uppaluri, Simona Onori, William C. Chueh
Lithium-ion batteries remain at rest for extended periods and experience calendar aging. Although lithium-ion batteries are expected to perform for over 10 years at room temperature, long-term calendar aging data are seldom reported over such timescales. We present a dataset from 232 commercial cells across eight cell types and five manufacturers that underwent calendar aging across various temperatures and states of charge (SOCs) for up to 13 years. We analyze calendar aging across these conditions by tracking capacity loss and resistance growth as the cells degrade. This dataset is used to validate simple models, primarily the Arrhenius law and the power law, which explain the temperature and storage time on calendar aging. Certain applications of Arrhenius and power law fail to describe the dependence of capacity loss on temperature and resistance growth on storage time. Through this dataset, we demonstrate the complexity of calendar aging and the challenges in reducing trends into phenomenological models.
中文翻译:
十年洞察:深入研究日历老龄化趋势和影响
锂离子电池长时间保持静止状态,并经历日历老化。尽管锂离子电池预计在室温下可使用 10 年以上,但在这样的时间尺度上很少报告长期日历老化数据。我们展示了来自 8 种电池类型和 5 家制造商的 232 个商业电池的数据集,这些电池在不同的温度和荷电状态 (SOC) 下经历了长达 13 年的日历老化。我们通过跟踪细胞降解过程中的容量损失和抗性增长来分析这些条件下的日历衰老。此数据集用于验证简单模型,主要是 Arrhenius 定律和幂定律,它们解释了日历老化的温度和存储时间。Arrhenius 和幂律的某些应用未能描述容量损失对温度和电阻增长对存储时间的依赖性。通过这个数据集,我们展示了日历老化的复杂性以及将趋势简化为现象学模型的挑战。
更新日期:2024-12-18
中文翻译:
十年洞察:深入研究日历老龄化趋势和影响
锂离子电池长时间保持静止状态,并经历日历老化。尽管锂离子电池预计在室温下可使用 10 年以上,但在这样的时间尺度上很少报告长期日历老化数据。我们展示了来自 8 种电池类型和 5 家制造商的 232 个商业电池的数据集,这些电池在不同的温度和荷电状态 (SOC) 下经历了长达 13 年的日历老化。我们通过跟踪细胞降解过程中的容量损失和抗性增长来分析这些条件下的日历衰老。此数据集用于验证简单模型,主要是 Arrhenius 定律和幂定律,它们解释了日历老化的温度和存储时间。Arrhenius 和幂律的某些应用未能描述容量损失对温度和电阻增长对存储时间的依赖性。通过这个数据集,我们展示了日历老化的复杂性以及将趋势简化为现象学模型的挑战。