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Monte Carlo assisted sensitivity analysis of a Li-ion battery with a phase change material

机译:Monte Carlo辅助敏感性分析锂离子电池的相变材料

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Safety of Li ion batteries is as important, if not more, as its performance because its usage in contemporary transport applications can otherwise lead to catastrophic scenarios. Owing to the exothermic reactions and complex heat transfer within the Li ion batteries, they are prone to overheating and possible thermal runaway in the absence of adequate thermal management systems. Therefore, to ensure a safe operation it is key to evaluate the uncertainties with respect to a thermal management system along with the uncertainties in the intricate multiphysical phenomena within the system. To achieve this, we conduct Monte Carlo simulations followed by sensitivity analysis to correlate the variability of 14 different factors with the safety of an 18650 Sony cell with a phase change material (PCM) at stressed conditions. These varied factors correspond to the properties of the 18650 battery and the PCM. From the PCM properties, we identify the temperature at which the PCM starts melting to have most influence on the safety of the battery. In addition, we also estimate the probability of the sub-optimal unsafe scenarios occurring by examining the distribution of the maximum temperature within the battery. Finally, we obtain reduced surrogate models with an accuracy of 92% through supervised machine learning algorithms.
机译:锂离子电池的安全性是重要的,如果不是更多的,因为它的性能是因为它在当代运输应用中的使用可能会导致灾难性的情景。由于锂离子电池内的放热反应和复杂的传热,它们在没有足够的热管理系统的情况下容易过热和可能的热失控。因此,为了确保安全操作,它是评估热管理系统的不确定性以及系统内复杂的多体现象中的不确定性。为此,我们进行蒙特卡罗模拟,然后进行敏感性分析,以将14个不同因素的可变性与强调条件下的相变材料(PCM)的安全性相关联14个不同因素。这些变化的因素对应于18650电池和PCM的特性。从PCM属性来看,我们识别PCM开始熔化的温度,对电池的安全性产生最大影响。此外,我们还估计通过检查电池内最大温度的分布而发生的次优不安全场景的概率。最后,通过监督机器学习算法,我们获得减少的代理模型,精度为92%。

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