基于卡尔曼滤波和ARX模型的锂离子寿命预测

Lukáš Krčmář, P. Rydlo, A. Richter, J. Eichler, Pavel Jandura
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引用次数: 0

摘要

健康状态(SOH)是保证电池系统全寿命期内安全可靠运行的关键因素。寿命周期的估计对有效、无故障工作具有重要意义。影响电池退化速度的因素很多。每个细胞都有典型的不同的降解机制。本文讨论了健康状态(SOH)的一种方法。一种混合方法将自回归外源电池模型(ARX)与卡尔曼滤波相结合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State of Health and Aging Estimation Using Kalman Filter in Combination with ARX Model for Prediction of Lifetime Period of Li-Ion
The state of health (SOH) is a critical factor to guarantee that a battery system will operate in a safe and reliable manner for the whole lifetime period. The estimation of the lifetime period is important for effective and failure - free working. Many factors affect the rate of the degradation of batteries. Every cell has a typical dissimilar mechanism of degradation. A method of the state of health (SOH) is discussed in this contribution. A hybrid method takes advantage of combining the autoregressive exogenous battery model (ARX) and the Kalman filter.
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