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.

本数据集为基于物理模型生成的12节LG M50型电池合成退化数据集。
电池模型：采用Doyle-Fuller-Newman（DFN）模型模拟电池内部状态，并在Python电池数学建模（Python Battery Mathematical Modeling, PyBaMM）库中，将四种退化机制——固体电解质界面膜生长、颗粒开裂、锂镀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（包括电极参数、电解质参数与隔膜参数）取自Chen等[1]的研究，对应LG化学（LG Chem）生产的商用NMC 811/石墨氧化硅（graphite-SiOx）圆柱电池，型号为INR21700 M50，额定容量5 Ah。PyBaMM库中退化模型的参数来源于多类文献，具体可查阅参考文献[2]的补充材料。研究人员刻意调整了三项退化参数：即Paris定律中的开裂速率、死锂形成的衰减速率、阳极活性物质损失比例项。
循环测试规程：电池采用1C恒流-恒压（Constant Current-Constant Voltage, CC-CV）规程充电至4.2 V，充电截止电流为C/100（50 mA），随后静置5分钟。接着以1C放电至2.5 V，放电截止电流同样为C/100（50 mA），之后再次静置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. 《电化学学会会刊, 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. 锂离子电池退化：建模方法. 《物理化学化学物理, 2022, 24(13):7909-22.