Quantitative analysis of the aging process of lithium-ion batteries by using electrochemical thermodynamic and kinetic parameters such as electrochemical potential, Li stoichiometry, and Li inventory loss is a key research topic in the development of Li-ion battery for electric vehicles and smart grids. It is generally known the above parameters can be acquired through the analysis of Electromotive Force (EMF) or Open-Circuit Voltage (OCV) curves. In this work, we proposed and applied five EMF measurement techniques to obtain the EMF-SoC relationships in LFP/Gr single-layer laminated pouch cells at different temperatures and States of Health (SoH), and comprehensively examined them from the viewpoints of eight evaluation dimensions. A Python program, named Degradation Modes Analysis (DMA), is used to diagnose the thermodynamic degradation modes of the battery automatically. Furthermore, electrochemical kinetic parameters were also extracted along with the depolarization process. For faster and more accurate aging diagnosis, we recommend an optimal blend of short relaxation time GITT (Short-Rest-GITT) and extrapolated EMF (Extrap-EMF) to reach the most precise EMF measurements and gain the most comprehensive information about battery thermodynamics and kinetics at the same time.

中文翻译：

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锂离子电池热力学及动力学参数测量技术对比研究

利用电化学势、锂化学计量和锂库存损失等电化学热力学和动力学参数对锂离子电池的老化过程进行定量分析是电动汽车和智能电网用锂离子电池发展的关键研究课题。众所周知，上述参数可以通过电动势（EMF）或开路电压（OCV）曲线分析获得。在这项工作中，我们提出并应用了五种 EMF 测量技术，以获得 LFP/Gr 单层层压软包电池在不同温度和健康状态 (SoH) 下的 EMF-SoC 关系，并从八个评估的角度对其进行综合检验方面。名为退化模式分析（DMA）的Python程序用于自动诊断电池的热力学退化模式。此外，随着去极化过程还提取了电化学动力学参数。为了更快、更准确的老化诊断，我们建议短弛豫时间 GITT (Short-Rest-GITT) 和外推 EMF (Extrap-EMF) 的最佳组合，以达到最精确的 EMF 测量并获得有关电池热力学和性能的最全面的信息。同时进行动力学。