一次电池和二次电池基于模型的预测诊断

J. D. Kozlowski, C. Byington, A. Garga, M. Watson, T. A. Hay
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引用次数: 8

摘要

这里描述的基于模型的工作主要集中在一次电池和二次电池的预测诊断上。然而,这种新方法也可以应用于其他电化学能源,如燃料电池。该方法基于电池内部传输机制的精确参数化建模。这些系统知识被用于精心开发电化学和热模型。这些模型被用来提取新的特征,与几个传统的测量参数结合使用,以评估电池的状况。然后使用由神经网络和决策理论方法组成的混合自动推理方案对结果输出和任何可用的关于电池的信息进行评估。本文的研究重点是监测和虚拟传感器数据的模型识别和数据融合。本文提出的方法和分析适用于多种传感器类型用于诊断评估的机械系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model-based predictive diagnostics for primary and secondary batteries
The model-based effort described here is focused on predictive diagnostics for primary and secondary batteries. However, this novel approach can also be applied to other electrochemical energy sources such as fuel cells. This method is based on accurate parametric modeling of the transport mechanisms within the battery. This system knowledge was used for the careful development of electrochemical and thermal models. These models have been used to extract new features to be used in conjunction with several traditional measured parameters to assess the condition of the battery. The resulting output and any usable information available about the battery is then evaluated using hybrid automated reasoning schemes consisting of neural network and decision theoretic methods. The focus of this paper is on the model identification and data fusion of the monitored and virtual sensor data. The methodology and analysis presented in this paper is applicable to mechanical systems where multiple sensor types are used for diagnostic assessment.
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