预言
电池(电)
颗粒过滤器
工作(物理)
可靠性工程
计算机科学
粒子(生态学)
电池容量
工程类
人工智能
机械工程
功率(物理)
热力学
物理
卡尔曼滤波器
地质学
海洋学
作者
Bhaskar Saha,Kai Goebel
摘要
This paper presents an empirical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.
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