Predicting the lifetimes of LiFePO4 batteries on the basis of the gamma process through accelerated degradation measurements

Yu-Chang Lin, K. Chung
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引用次数: 1

Abstract

This study mainly focused on evaluating the capacity fade of LiFePO4 batteries by using a novel dual dynamic stress accelerated degradation test, called D2SADT. This test method was developed to simulate a situation involving driving an electric vehicle in the city. D2SADT contains two controllable dynamic stress variables: the environmental factor corresponding to temperature cycling and the power factor corresponding to charging-discharging currents and times at which they were implemented simultaneously. A reference power test was performed repeatedly at a certain time (e.g., five temperature cycles), and the cell capacity was then calculated to monitor the degradation of the batteries. A compositional reliability assessment using the gamma process and Monte Carlo simulation was implemented to calculate the likelihood values of the test samples, LiFePO4 batteries, on the basis of their capacity loss. The test results indicate that the battery capacity decreases over time, validating the novel test method (D2SADT). Moreover, the modeling results indicate that the gamma process combined with Monte Carlo simulation provide superior accuracy for predicting the lifetimes of the test batteries compared with the baseline lifetime data (true degradation route and lifetime). Furthermore, the results indicate the high prediction performance of the proposed model because an error rate of within 5% was obtained after half of the cycles were completed (70 temperature cycles), including the measurements.
基于伽马过程,通过加速降解测量来预测LiFePO4电池的寿命
本研究主要通过一种新的双动态应力加速退化试验(D2SADT)来评估LiFePO4电池的容量衰减。这种测试方法是为了模拟在城市中驾驶电动汽车的情况而开发的。D2SADT包含两个可控的动态应力变量:对应于温度循环的环境因子和对应于同时进行充放电电流和时间的功率因子。参考功率测试在特定时间重复进行(例如,五个温度循环),然后计算电池容量以监测电池的退化。利用伽玛过程和蒙特卡罗模拟进行了成分可靠性评估,以计算LiFePO4电池测试样品的容量损失的似然值。测试结果表明,电池容量随着时间的推移而减小,验证了新的测试方法(D2SADT)。此外,建模结果表明,与基线寿命数据(真实退化路径和寿命)相比,伽玛过程与蒙特卡罗模拟相结合在预测测试电池寿命方面具有更高的准确性。此外,结果表明,该模型具有较高的预测性能,因为在完成一半的循环(70个温度循环)后,包括测量在内,该模型的错误率在5%以内。
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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