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Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition

机译:基于锂离子电池的增量容量分析考虑充电条件

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摘要

Incremental capacity analysis (ICA) has been widely employed to investigate the degradation mechanism and perform the capacity estimation of lithium-ion batteries. However, the traditional capacity estimation based on ICA is limited by the computational efficiency and charging condition. In this paper, a rapid acquisition method of the incremental capacity (IC) curve is established, then an adaptive capacity estimation framework based on ICA considering the charging condition is proposed. Aiming at improving the computational efficiency, the Kalman filter is employed to acquire the smooth IC curves expeditiously, which requires small data length to handle the IC value. A considerable number of battery standard and non-standard charging experiments are designed and conducted. The influence of battery aging status, charging initial state of charge (SOC), and temperature on IC curves is revealed. Three features of the IC curve during standard charging are selected to investigate the relationship between battery capacity and the height of features. Furthermore, an adaptive correction method for capacity estimation considering the charging initial SOC is established, and the validation results show the effectiveness of the proposed correction method, which provides high accuracy and robustness.
机译:增量容量分析(ICA)已被广泛用于研究降解机制,并执行锂离子电池的容量估计。然而,基于ICA的传统容量估计受到计算效率和充电条件的限制。在本文中,建立了一种基于ICA的增量容量(IC)曲线的快速采集方法,考虑到充电条件,是基于ICA的自适应容量估计框架。旨在提高计算效率,采用卡尔曼滤波器迅速获取平滑IC曲线,这需要小的数据长度来处理IC值。设计并进行了相当数量的电池标准和非标准充电实验。揭示了电池老化状态,充电初始充电状态(SOC)和IC曲线温度的影响。选择标准充电期间IC曲线的三个特征来研究电池容量与特征高度之间的关系。此外,建立了考虑充电初始SOC的容量估计的自适应校正方法,验证结果表明了所提出的校正方法的有效性,其提供高精度和鲁棒性。

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