Synthetic Degradation Dataset of 12 LG M50 Batteries

A synthetic degradation dataset of 12 LG M50 cells was generated using physics-based models. Battery models: Underlying battery states are simulated using a Doyle-Fuller-Newman (DFN) model, and four degradation mechanisms (i.e., solid electrolyte interphase growth, particle cracking, lithium plating, and stress-driven loss of active material) are coupled with the DFN model in Python Battery Mathematical Modeling (PyBaMM) library. Model parameters: The DFN model parameters (i.e., electrode parameters, electrolyte parameters, and separator parameters) are taken from Chen et al. [1] for a commercial NMC 811/graphite-SiOx cylindrical cell manufactured by LG Chem (INR21700 M50, 5 Ah). The parameters of the degradation models in PyBaMM are taken from multiple sources and can be found in the supplementary information of Ref. [2]. Three degradation parameters (i.e., cracking rate in Paris' law, decay rate for dead lithium formation, and loss of active anode material proportional term) were intentionally varied. Cycling protocols: The cells are charged with a 1C constant current-constant voltage (CC-CV) step to 4.2 V and a current cut-off of C/100 (50 mA) followed by a rest for 5 minutes. Subsequently, the cells are discharged at 1C to 2.5 V with a current cut-off of C/100 (50 mA) and then at rest for 5 minutes. The ambient temperature is set to be constant at 25°C. References [1] Chen CH, Planella FB, O’regan K, Gastol D, Widanage WD, Kendrick E. Development of experimental techniques for parameterization of multi-scale lithium-ion battery models. Journal of The Electrochemical Society. 2020 May 15;167(8):080534. [2] O'Kane SE, Ai W, Madabattula G, Alonso-Alvarez D, Timms R, Sulzer V, Edge JS, Wu B, Offer GJ, Marinescu M. Lithium-ion battery degradation: how to model it. Physical Chemistry Chemical Physics. 2022;24(13):7909-22.

本数据集为采用物理模型生成的12节LG M50电芯合成降解数据集。

电池模型：采用多伊尔-富勒-纽曼（Doyle-Fuller-Newman, DFN）模型模拟电池内部状态，并通过Python电池数学建模（Python Battery Mathematical Modeling, PyBaMM）库，将四种降解机制——固体电解质界面（solid electrolyte interphase, SEI）膜生长、颗粒开裂、析锂以及应力驱动的活性物质损失——与DFN模型进行耦合。

模型参数：DFN模型参数（包含电极参数、电解质参数与隔膜参数）取自Chen等人[1]的研究，对应LG化学（LG Chem）生产的商用NMC 811/石墨-SiOx圆柱电芯（INR21700 M50，5 Ah）。PyBaMM库中降解模型的参数来源多样，相关细节可参考文献[2]的补充材料获取。研究人员刻意调整了三项降解参数：帕里斯定律（Paris' law）中的开裂速率、死锂形成的衰减速率，以及活性负极物质损失比例项。

循环测试规程：电芯采用1C恒流-恒压（constant current-constant voltage, CC-CV）充电步骤充电至4.2 V，电流截止阈值设为C/100（对应50 mA），随后静置5分钟。之后以1C倍率放电至2.5 V，电流截止阈值同样为C/100（对应50 mA），随后再次静置5分钟。环境温度恒定设置为25℃。

参考文献：
[1] Chen CH, Planella FB, O’regan K, Gastol D, Widanage WD, Kendrick E. 多尺度锂离子电池模型参数化实验技术开发. Journal of The Electrochemical Society. 2020年5月15日;167(8):080534.
[2] O'Kane SE, Ai W, Madabattula G, Alonso-Alvarez D, Timms R, Sulzer V, Edge JS, Wu B, Offer GJ, Marinescu M. 锂离子电池降解建模方法探析. Physical Chemistry Chemical Physics. 2022;24(13):7909-7922.

搜集汇总

背景与挑战

背景概述

该数据集是一个基于物理模型生成的合成退化数据集，模拟了12个LG M50电池在标准循环协议下的性能退化过程，涵盖了固体电解质界面生长、颗粒开裂、锂沉积和活性材料损失四种关键退化机制。数据集通过故意改变退化参数来探究不同因素对电池寿命的影响，适用于锂离子电池退化建模和分析研究。