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Improved transfer functions modeling linearized lithium-ion battery-cell internal electrochemical variables

机译:改进的传递函数建模线性化的锂离子电池内部电化学变量

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摘要

Battery management systems (BMS) require computationally simple but highly accurate models of the battery cells they are monitoring and controlling. Historically, empirical equivalent-circuit models have been used, but increasingly researchers are focusing their attention on physics-based models due to their greater predictive capabilities. These models are of high intrinsic computational complexity and so must undergo some kind of order-reduction process to make their use by a BMS feasible: we favor methods based on a transfer-function approximation to battery-cell dynamics.In prior works, transfer functions have been found from full-order PDE models via two assumptions: (1) a linearization assumption—which is a fundamental necessity in order to make transfer functions—and (2) an assumption made out of expedience that decouples the electrolyte-potential and electrolyte-concentration PDEs in order to render an approach to solve for the transfer functions from the PDEs. This paper shows how to eliminate the need for the second assumption, thus retaining the coupling between these two PDEs and improving overall model accuracy. Time-domain models created from these transfer functions are especially improved when simulating constant-current profiles since the electrolyte concentration gradient increases the coupling between the electrolyte-potential and electrolyte-concentration PDEs.
机译:电池管理系统(BMS)需要它们所监视和控制的电池单元的计算简单但高度准确的模型。从历史上看,已经使用了经验等效电路模型,但是由于它们具有更好的预测能力,越来越多的研究人员将注意力集中在基于物理的模型上。这些模型具有很高的内在计算复杂度,因此必须经过某种降阶处理才能使BMS可行:我们偏爱基于传递函数近似于电池动态的方法。通过两个假设从全序PDE模型中发现:(1)线性化假设(这是制作传递函数的基本必要条件)和(2)出于权宜之计而将电解质电势和电解质解耦的假设-浓度PDE,以提供一种解决PDE传递函数的方法。本文展示了如何消除对第二个假设的需求,从而保留了这两个PDE之间的耦合并提高了整体模型的准确性。当模拟恒定电流曲线时,由于这些电解质浓度梯度增加了电解质电势和电解质浓度PDE之间的耦合,因此,从这些传递函数创建的时域模型尤其得到改善。

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