Calendar aging of commercial graphite/LiFePO4 cell - Predicting capacity fade under time dependent storage conditions
Calendar aging of commercial graphite/LiFePO4 cell - Predicting capacity fade under time dependent storage conditions
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DOI：
10.1016/j.jpowsour.2013.11.098
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发表时间：
2014-06
影响因子：
9.2
通讯作者：
Sébastien Grolleau;A. Delaille;H. Gualous;P. Gyan;R. Revel;J. Bernard;E. Redondo-Iglesias;Jéremy Peter
中科院分区：
文献类型：
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作者：
Sébastien Grolleau;A. Delaille;H. Gualous;P. Gyan;R. Revel;J. Bernard;E. Redondo-Iglesias;Jéremy Peter
在电子运输等应用中，电池的大部分寿命都是在存储中度过的。因此，了解和估计储存下的老化，也被称为日历老化，是预测细胞寿命的先决条件。本工作研究了商用15Ah型锂离子石墨/磷酸铁电池在储存时的老化行为。利用非破坏性电化学测试，分析了在9种静止条件下储存450天的性能下降情况。研究发现，温度比荷电状态(SOC)更有害。大多数情况下，降解模型表示相对于时间和老化条件的累积降解。本文提出了一种以衰减率为核心的预测容量衰落的简单建模方法。这使得能够在实际应用中通常经历的依赖时间的存储条件(SoC和温度)下预测单元退化。将模型预测与在受控随时间变化的温度储存条件下获得的625天的实验日历老化数据进行了比较。预测结果与实验结果吻合较好，400天容量预测的绝对误差不超过3%，625天的绝对误差不超过5%。
In applications such as electrical transportation, most of the battery life is spent under storage. Understanding and estimating aging under storage, also named as calendar aging, is therefore a prerequisite for cell life prediction. This work investigates aging behavior upon storage of a commercial 15 Ah lithium-ion graphite/iron phosphate cell. Performance decline during 450 days of storage under nine stationary conditions is analyzed using non-destructive electrochemical tests. Temperature is found to be more detrimental than State of Charge (SoC). Most often, degradation models express the accumulated degradation with respect to time and aging conditions. In this article, a simple modeling approach is proposed focusing on the degradation rate to predict capacity fade. This permits predicting cell degradation under time dependent storage conditions (SoC and temperature) which are usually experienced in real applications. Model prediction is compared to experimental calendar aging data obtained over 625 days in a controlled time dependent temperature storage conditions. Predictions are in good agreement with experimental results as the absolute error on capacity prediction never exceeds 3% over 400 days and 5% over 625 days.