锂离子电池老化和温度的市场定义范式平衡电压分析

Samuel O. Enochoghene, Thomas K. Yesufu
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摘要

这项研究是关于使用市场定义范式(MDP)来跟踪锂离子电池的老化和温度效应。其目的是利用该技术从现成的数据集中获取一系列平衡电压,以剖析老化和温度对电池和电池组的影响。所采用的方法包括利用 MDP 的能力获取锂离子电池的一系列平衡电压。这种方法将无线电入射与无线电几何形状、传输和出现集成在电池平衡电压(和安培数)的简化形式中。标准数据集来自先进生命周期工程中心的资料库。使用 Python 3.8 编程工具和 LibreOffice 电子表格软件,采用库仑计数、充放电能量比较方法对数据进行了处理和分析。结果显示,所研究电池的老化和温度现象密切相关。在两千(2000)次循环中,老化和温度研究同样分别获得了 3.23 V 和 3.10 V 的最大和最小平衡电压。随着电池老化,平衡电压呈下降趋势,与传统上用于跟踪此类电池现象的开路电压相比,平衡电压对此类电池的研究更为可靠。总之,可以利用平衡电压对典型的锂离子电池在开始使用时进行分类,并对其使用寿命做出有用的预测。这种方法成本相对较低,只需较少的数据点和低成本硬件,并可扩展到在线应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A market definition paradigm equilibrium voltage analysis of ageing and temperature in lithium-ion cells
This study was on the use of the market definition paradigm (MDP) to track ageing and temperature effects in lithium–ion cells. This was with a view to using the technique to obtain a sequence of equilibrium voltages from readily available datasets in order to profile the effects of ageing and temperature on cells and batteries. The method employed involved using the MDP with its capability to obtain a sequence of equilibrium voltages for lithium-ion cells. This approach integrated radio incidence with radio geometry, transmission and emergence in a simplified form of the cell’s equilibrium voltage (and amperage). A standard dataset was obtained from the centre for advanced life cycle engineering repository. The data were processed and analysed using Coulomb counting, charging and discharging energy comparison methods on Python 3.8 programming tool and LibreOffice spreadsheet software. Results obtained show a close tracking of ageing and temperature phenomena in the cells studied. A respective maximum and minimum equilibrium voltages of 3.23 V and 3.10 V over two thousand (2000) cycles were similarly obtained for ageing and temperature investigations. The equilibrium voltage shows a downward trend as the battery ages and is more reliable for studies on these cells than the open circuit voltage traditionally used to track phenomena in such cells. In conclusion, typical lithium-ion cells can be classified at begin-of-life using the equilibrium voltage and useful predictions made with respect to end-of-life. This approach is relatively inexpensive, requiring fewer data points and low-cost hardware and extensible to online applications.
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