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Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis

机译:使用恒压充电电流分析在线评估锂离子电池的健康状况

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摘要

Battery state-of-health (SoH) estimation is a critical function in a well-designed battery management system (BMS). In this paper, the battery SoH is detected based on the dynamic characteristic of the charging current during the constant-voltage (CV) period. Firstly, according to the preliminary analysis of the battery test data, the time constant of CV charging current is proved to be a robust characteristic parameter related to the battery aging. Secondly, the detailed expression of the current time constant is derived based on the first order equivalent circuit model (ECM). Thirdly, the quantitative correlation between the normalized battery capacity and the current time constant is established to indicate the battery SoH. Specifically, for the uncompleted CV charging process, the logarithmic function-based current time constant prediction model and the reference correlation curve are established to identify the battery capacity fading. At last, experimental results showed that regardless of the adopted data size, the correlation identified from one battery can be used to indicate the SoH of other three batteries within 2.5% error bound except a few outliers.
机译:电池健康状态(SoH)估计是设计良好的电池管理系统(BMS)中的关键功能。本文基于恒压(CV)期间充电电流的动态特性来检测电池SoH。首先,根据对电池测试数据的初步分析,证明了CV充电电流的时间常数是与电池老化相关的鲁棒特性参数。其次,基于一阶等效电路模型(ECM)导出当前时间常数的详细表达式。第三,建立归一化电池容量和当前时间常数之间的定量关系,以指示电池SoH。具体而言,对于未完成的CV充电过程,建立了基于对数函数的当前时间常数预测模型和参考相关曲线,以识别电池容量的衰减。最后,实验结果表明,无论采用何种数据大小,从一个电池中识别出的相关性都可以用来指示其他三个电池的SoH(误差范围在2.5%以内),但有几个离群值。

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