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对于金属电池循环模型的参数拟合的深度学习方法,通过实验性电池循环时间序列进行验证

Maria Grazia Quarta¹, Ivonne Sgura², Elisa Emanuele³

¹Dipartimento di Matematica E Fisica "E. De Giorgi", Università del Salento, Via Per Arnesano, 73100, Lecce, Italy.

Scientific reports

|February 7, 2025

概括

这项研究引入了一种新的方法,将卷积神经网络长短记忆 (CNN-LSTM) 与部分微分方程 (PDE) 建模结合起来,用于分析对称硬币细胞循环数据. 这种方法增强了电池材料的定量分析,特别是金属阳极.

科学领域:

电化学电化学电化学
材料科学材料科学材料科学
计算建模计算建模

背景情况:

对称的硬币电池循环对于电池材料分析至关重要,特别是金属阳极,以了解在运行条件下固体电解质介相 (SEI) 增长和形状变化.
分析硬币细胞循环数据的现有方法在量化利用大型实验数据集和充分理解电化学反应方面存在局限性.

研究的目的:

开发和应用一种新的方法,对对称的硬币细胞循环数据进行定量分析.
在使用机器学习的部分微分方程 (PDE) 模型中确定关键的物理化学参数.
描述金属阳极模拟和实验电荷放电配置的行为.

主要方法:

使用一个卷积神经网络-长期短期记忆 (CNN-LSTM) 模型进行参数识别.
采用部分微分方程 (PDE) 建模来模拟金属阳极循环.
将CNN-LSTM模型应用于具有Li芯片和LP30电解质的对称硬币细胞的实验数据.

主要成果:

通过CNN-LSTM方法成功确定了PDE模型的相关物理化学参数.
证明了模型能够描述模拟和实验电荷-放电概况的能力.
展示了拟议的CNN-LSTM方法在参数识别中相对于传统最小方程的优势.

结论:

结合CNN-LSTM和PDE建模,为电池循环数据的定量分析提供了一个强大的工具.
与传统技术相比,这种方法为参数识别提供了更有效的方法.
这些发现有助于更好地了解对称硬币细胞中的金属阳极行为.

关键词:

电池 PDE 建模在CNN-LSTM神经网络中.静电循环使用金属的是金属的参数估计的参数估计.对称的细胞是对称的.

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