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Controls-oriented models of lithium-ion cells having blend electrodes. Part 2: Physics-based reduced-order models

机译:具有混合电极的锂离子电池的面向控件的模型。第2部分:基于物理的降阶模型

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摘要

Physics-based battery models can predict not only voltage behaviors of a cell but also internal electrochemical variables such as lithium concentrations and electrical potentials. Knowledge of these variables will be critical in future battery management systems to be able to devise controls that extract the maximum performance from a cell while also slowing down its degradation, since available performance and degradation are both direct functions of the values of these internal electrochemical variables. This paper and its Part-1 companion concern themselves with simple but accurate models of lithium- ion cells having electrodes that are composed of a blend of multiple active materials. In this paper, we show how to create a physics-based reduced-order model (ROM). This ROM not only gives better voltage predictions than the equivalent-circuit models proposed in the Part-1 paper, but is also able to predict all cell internal electrochemical variables. Additionally, its computational complexity is similar to that of the circuit model.
机译:基于物理的电池模型不仅可以预测电池的电压行为,还可以预测内部电化学变量,例如锂浓度和电势。这些变量的知识对于将来的电池管理系统至关重要,因为它能够设计出能够从电池中提取最大性能同时减缓其退化的控件,因为可用的性能和退化都是这些内部电化学变量值的直接函数。 。本文及其第1部分的同伴对简单而精确的锂离子电池模型感到关注,这些模型的电极由多种活性材料的混合物组成。在本文中,我们展示了如何创建基于物理学的降阶模型(ROM)。该ROM不仅比第1部分论文中提出的等效电路模型提供了更好的电压预测,而且还能够预测所有电池内部电化学变量。另外,它的计算复杂度类似于电路模型。

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