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.

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。