Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries
Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries
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DOI：
10.1109/tie.2012.2224079
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发表时间：
2013-11
影响因子：
7.7
通讯作者：
M. Orchard;Pablo Hevia-Koch;Bin Zhang;L. Tang
中科院分区：
文献类型：
--
作者：
M. Orchard;Pablo Hevia-Koch;Bin Zhang;L. Tang
本文提出了一类风险的措施，作为损害指标内粒子滤波（PF）为基础的实时预测算法，与应用的情况下，在锂离子电池的荷电状态预测。所提出的风险度量不仅包括电池故障的风险，而且是对预后算法本身的置信度的度量。此外，提出了一种新的简化的基于PF的预测方法来估计电池放电时间，同时提供了一个计算成本低廉的解决方案。新的预后程序和相关的风险度量的计算时间都足够快，以允许它们在实时应用中实现，例如决策系统或路径规划算法。
This paper presents a class of risk measures to be used as damage indicators within particle filtering (PF)-based real-time prognosis algorithms, with application to the case of state-of-charge prediction in lithium-ion batteries. The proposed risk measure not only incorporates the risk of battery failure but also is a measure for the confidence on the prognosis algorithm itself. In addition, a novel simplified PF-based prognostic method is proposed to estimate the battery discharge time, while providing a computationally inexpensive solution. Computing times for both the novel prognosis routine and the associated risk measure are fast enough to allow their implementation in real-time applications, such as decision-making systems or path-planning algorithms.