基于粒子滤波的电池寿命预测实例研究

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing) Pub Date : 2012-05-23 DOI:10.1109/PHM.2012.6228847

Yinjiao Xing, E. W. M. Ma, K. Tsui, M. Pecht

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引用次数: 21

摘要

电池对电池供电系统的可靠性起着至关重要的作用。电池的预测功能为故障的出现提供预警，需要及时维护和更换电池。本文综述了电池退化模型的研究现状，重点介绍了预测算法的在线实现。基于两种退化模型，采用粒子滤波方法对电池性能进行跟踪，这两种退化模型对在线应用非常有效。最后给出了该方法的实验验证。通过对预测结果的比较，讨论了模型和算法存在的问题。

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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A case study on battery life prediction using particle filtering

Batteries play a critical role for the reliability of battery-powered systems. The prognostics in batteries provide warning to the advent of failure, which requires timely maintenance and replacement of batteries. This paper reviews current research on battery degradation models and focuses on the online implementation of prognostic algorithms. The particle filtering approach is utilized to track battery performance based on two degradation models that are highly efficient for online applications. An experimental demonstration of this method is provided. Through a comparison of the prognostic results, the problems of the models and the algorithm are discussed.

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Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)

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