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Battery open-circuit voltage estimation by a method of statistical analysis

机译:统计分析法估算电池开路电压

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摘要

The basic task of a battery management system (BMS) is the optimal utilization of the stored energy and minimization of degradation effects. It is critical for a BMS that the state-of-charge (SoC) be accurately determined. Open-circuit voltage (OCV) is directly related to the state-of-charge of the battery, accurate estimation of the OCV leads to an accurate estimate of the SoC. In this paper we describe a statistical method to predict the open-circuit voltage on the basis of voltage curves obtained by charging batteries with different currents. We employ a dimension reduction method (Karhunen-Loeve expansion) and linear regression. Results of our modelling approach are independently validated in a specially designed experiment.
机译:电池管理系统(BMS)的基本任务是最佳利用存储的能量并最大程度地降低降级效果。对于BMS来说,准确确定充电状态(SoC)至关重要。开路电压(OCV)与电池的充电状态直接相关,对OCV的准确估算会导致对SoC的准确估算。在本文中,我们描述了一种统计方法,该方法可根据通过对不同电流的电池充电而获得的电压曲线来预测开路电压。我们采用降维方法(Karhunen-Loeve展开)和线性回归。我们的建模方法的结果在专门设计的实验中得到了独立验证。

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