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首页> 外文期刊>Engineering failure analysis >Remaining useful lifetime prediction based on the damage-marker bivariate degradation model: A case study on lithium-ion batteries used in electric vehicles
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Remaining useful lifetime prediction based on the damage-marker bivariate degradation model: A case study on lithium-ion batteries used in electric vehicles

机译：基于损伤标记双变量退化模型的剩余使用寿命预测：以电动汽车用锂离子电池为例

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Remaining useful lifetime (RUL) refers to the available service time left before the performance of a system degrades to an unacceptable level. Recent innovations to lithium-ion battery packs have raised expectations with regard to energy storage capability in electric vehicles (EVs). This has catalyzed new research on RUL prediction, since accurate RUL prediction for lithium-ion batteries used in EV is highly desired for safe and lifetime-optimized operation. A battery's maximum releasable capacity (MRC) usually decays over time, thus it is a primary factor which determines the remaining cycle life of the battery. However, MRC usually needs to be measured under strict laboratory conditions and cannot be easily assessed during field use in EVs. This naturally inhibits potential applications of many online RUL prediction methods that rely on MRC measurements. We found two markers of MRC decay, named as time-tovoltage- saturation (TVS) and time-to-current-saturation (TCS), from constant-current constant-voltage charging (CC/CV) curves, which can be used in place of MRC measurements during field use. We propose a RUL prediction method based on a damage-marker bivariate degradation model in which one term represents damage (MRC decay), the other represents a composite marker constructed from TVS and TCS. We model this degradation process using a two-dimensional Wiener process to obtain the RUL distribution, using method of maximum likelihood for population parameters' estimation. Bayesian methods are used to update the estimators of parameters with online data. The effectiveness of the model is verified with public data of four 18,650 batteries from NASA. (C) 2016 Published by Elsevier Ltd.
机译：剩余使用寿命（RUL）是指系统性能下降到不可接受的水平之前剩余的可用服务时间。锂离子电池组的最新创新对电动汽车（EV）的储能能力提出了更高的期望。这催生了有关RUL预测的新研究，因为对于安全且寿命优化的运行，迫切需要针对EV中使用的锂离子电池进行准确的RUL预测。电池的最大可释放容量（MRC）通常会随着时间而衰减，因此它是决定电池剩余循环寿命的主要因素。但是，MRC通常需要在严格的实验室条件下进行测量，并且在电动汽车的现场使用期间无法轻易评估。这自然抑制了许多依赖MRC测量的在线RUL预测方法的潜在应用。我们从恒流恒压充电（CC / CV）曲线中发现了MRC衰减的两个标记，分别称为时间到电压饱和（TVS）和时间到电流饱和（TCS），可用于现场使用期间MRC测量的位置。我们提出了一种基于损伤标记双变量降解模型的RUL预测方法，其中一个术语代表损伤（MRC衰减），另一个代表由TVS和TCS构成的复合标记。我们使用二维维纳过程对该降解过程进行建模，以使用最大似然方法进行总体参数估算来获得RUL分布。贝叶斯方法用于用在线数据更新参数的估计量。该模型的有效性通过来自NASA的四个18,650电池的公开数据进行了验证。 （C）2016由Elsevier Ltd.出版

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