Experiments to diagnose the SoH of Samsung INR21700-50E Li-ion Cells

<h3> Description of the project </h3>

All the data included in this repository has been also included in a new repository named "Aging and characterization of lithium-ion cells to develop a state-of-health diagnostic protocol" (https://doi.org/10.21950/GT4LKX), together with additional data, making this repository obsolete.<br>
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This experimental work is part of the "Work Package 4 - Diagnosis of Li-ion cells and modules" of the project "Research and development of a highly automated and safe streamlined process for increased Lithium-ion battery repurposing and recycling". Acronym (REBELION). Project number: 101104241. Program: HORIZON-CL5-2022-D2-01-10, European Climate, Infrastructure and Environment Executive Agency (CINEA), European Commission.<br>
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The aim of the project is to develop a diagnosis methodology for lithium‐ion batteries that allows telling apart batteries fitted for second life applications from batteries that have reached the end of their life and need to be disassembled for recycling their components.<br>
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The following three novel diagnosis methods will be investigated in this project:
<ol>
<li> Electrochemical noise analysis (ENA): It consists of analyzing the voltage noise signal when the cell is subjected to a constant current. The noise signal is frequency decomposed, obtaining a characteristic pattern of the SoH.
<li> Current steps (CS): Application of small current steps when the battery operates at constant current. The dynamic behavior of the voltage allows fitting to the parameters of an equivalent electrical model that will provide information to identify the SoH of the battery.
<li> High‐frequency current steps (HF‐CS), which, like the CS technique, apply current steps, but in this case, only high‐frequency current steps are applied to determine the purely ohmic resistance of the battery.
</ol>
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The application of these three novel methods will be contrasted with other methods commonly used for identifying the SoH of batteries, but that do not satisfy the requirements posed in this project concerning diagnosis process time, and equipment. These commonly used methods include Incremental capacity analysis (ICA), Open circuit voltage (OCV), Electric impedance spectroscopy (EIS), and maximum State of charge (SoC).

<h3> Description of the dataset </h3>

This dataset contains the results obtained from the execution of multiple experiments designed to facilitate the diagnosis of the state of health of Lithium-ion cells.
The dataset involves the evaluation of 77 cells of the type Samsung INR21700-50E. The evaluated cells have been previously aged by means of a variable number of charge-discharge cycles (cf. related dataset), in order to obtain a distribution of states of health between 100% and 80%.

The dataset is structured as follows.
Cells are identified with a name composed of a combination of: group number, cycling strategy and cell ID:
<ul>
<li> The group number corresponds to the group defined by the aging experiment (cf. related dataset). Each group was planned to be cycled with a number of cycles.
<li> The cycling strategy describes the type of charge and discharge procedure used during cycling.
<li> The cell ID corresponds to A or B, since two cells are used in each cycling run.
</ul>

Each evaluated cell is contained in a directory, named with the cell name (e.g., "C4-CC-A"), that stores the results of the experiments performed to that cell. These experiments include EIS in OCV and at 0.2C discharge, ENA, HFSTEPS and LFSTEPS. They also include a measure of the room temperature during the experimental run.

<h3>项目描述</h3>

本仓库收录的所有数据已同步至名为“用于开发健康状态诊断规程的锂离子电池老化与表征”（https://doi.org/10.21950/GT4LKX）的新仓库中，该仓库还新增了额外数据集，因此本仓库现已失效。

本实验工作隶属于“面向锂离子电池再利用与回收的高度自动化安全精简流程研发”项目的“第四工作包——锂离子电池单体与模组诊断”。该项目缩写为REBELION，项目编号为101104241，所属资助计划为HORIZON-CL5-2022-D2-01-10，由欧盟气候、基础设施与环境执行局（CINEA）及欧盟委员会联合资助。

本项目旨在开发锂离子电池健康状态（State of Health, SoH）诊断方法，以区分适用于二次复用场景的电池与已达到寿命终点、需拆解回收其组分的报废电池。

本项目将研究三种新型诊断方法：
<ol>
<li>电化学噪声分析（Electrochemical Noise Analysis, ENA）：通过分析恒流工况下电池单体的电压噪声信号，对噪声信号进行频域分解，得到健康状态（SoH）的特征模式。
<li>电流阶跃法（Current Steps, CS）：当电池以恒流模式运行时，施加小幅电流阶跃，通过电压的动态响应拟合等效电路模型参数，以此获取电池健康状态的识别信息。
<li>高频电流阶跃法（High-Frequency Current Steps, HF-CS）：与常规电流阶跃法类似，同样施加电流阶跃，但仅采用高频电流阶跃以测定电池的纯欧姆内阻。
</ol>

将上述三种新型诊断方法与当前常用于电池健康状态识别的常规方法进行对比验证，但这些常规方法无法满足本项目在诊断流程时长与设备要求上的限定标准。常规方法包括增量容量分析（Incremental Capacity Analysis, ICA）、开路电压（Open Circuit Voltage, OCV）、电化学阻抗谱（Electrochemical Impedance Spectroscopy, EIS）以及最大荷电状态（State of Charge, SoC）。

<h3>数据集描述</h3>

本数据集包含为实现锂离子电池单体健康状态诊断所开展的多组实验结果。
本次评估共涉及77颗三星INR21700-50E型号锂离子电池单体。所有待测电池均通过不同次数的充放电循环完成老化预处理（详见关联数据集），以实现健康状态在100%至80%区间内的分布覆盖。

本数据集的结构说明如下：
电池单体以名称标识，命名规则由组号、循环策略与单体ID三部分组合而成：
<ul>
<li>组号：对应老化实验划分的组别（详见关联数据集），每组均规划了对应的循环测试次数。
<li>循环策略：描述老化过程中采用的充放电流程类型。
<li>单体ID：分为A或B，每组循环测试中共使用两颗电池单体。
</ul>

每颗待测电池对应一个以其名称命名的目录（例如"C4-CC-A"），目录内存储了针对该电池开展的各项实验结果，包括开路静置下的电化学阻抗谱、0.2C放电工况下的电化学阻抗谱、电化学噪声分析、高频阶跃测试与低频阶跃测试，同时还记录了实验过程中的室温环境温度。

搜集汇总

背景与挑战

背景概述

该数据集聚焦于三星INR21700-50E锂离子电池的健康状态诊断实验，包含77个经过不同循环老化的电池样本，SoH覆盖100%至80%范围。实验采用电化学噪声分析、电流阶跃等创新方法，并与传统诊断技术对比，旨在开发高效电池二次利用与回收的评估协议，数据按电池分组存储，便于分析不同老化条件下的性能变化。