Lithium-ion battery disturbance current estimation, with application to a self-balancing photovoltaic battery storage system

Partha P. Mishra, H. Fathy
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引用次数: 1

Abstract

This paper develops a model-based algorithm for combined state and disturbance estimation in a lithium-ion battery cell. The “disturbance”, in this context, is the external current applied to the cell. The algorithm estimates this current based solely on terminal voltage measurement, which is valuable for applications where current sensors are too costly. Furthermore, the paper presents a theoretical analysis of the disturbance estimation covariance achievable by this algorithm. The algorithm is particularly valuable for applications where estimates of battery current are needed, but measurements of this current are too costly. One example comes from the authors' previous work on a self-balancing hybrid photovoltaic/battery system. We apply the proposed algorithm, in simulation, to this system, and use a moving average filter to attenuate the noise in its disturbance current estimates. The results of this simulation study show that the proposed algorithm is indeed successful in tracking both the internal battery state and photovoltaic current in the above hybrid photovoltaic/battery system.
锂离子电池扰动电流估计及其在自平衡光伏电池储能系统中的应用
本文提出了一种基于模型的锂离子电池状态与扰动联合估计算法。在这种情况下,“扰动”是施加在电池上的外部电流。该算法仅基于终端电压测量来估计该电流,这对于电流传感器过于昂贵的应用很有价值。此外,本文还对该算法可实现的干扰估计协方差进行了理论分析。该算法对于需要估计电池电流的应用特别有价值,但测量这种电流的成本太高。其中一个例子来自作者之前关于自平衡光伏/电池混合系统的研究。在仿真中,我们将提出的算法应用于该系统,并使用移动平均滤波器来衰减其干扰电流估计中的噪声。仿真研究结果表明,在上述光伏/电池混合系统中,所提出的算法确实能够成功地跟踪电池内部状态和光伏电流。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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