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数据驱动的循环寿命预测基于金属的可充电电池基于放电/充电容量和放松功能的预测

Qianli Si^1,2, Shoichi Matsuda^2,3, Youhei Yamaji²

¹Department of Nanoscience and Nanoengineering, Faculty of Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, 169-8555, Japan.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|June 27, 2024

概括

机器学习准确地预测了高能金属电池的周期寿命. 这种方法使用各种电化学特征,在新数据上实现高精度和低误差,以更好地管理电池.

科学领域:

材料科学材料科学材料科学
电化学电化学电化学
数据科学数据科学数据科学

背景情况:

由于复杂的非线性降解机制,电池周期寿命预测具有挑战性.
具有高质量负载LiNi0.8Mn0.1Co0.1O2电极的金属电池比标准LiFePO4/石墨系统具有更复杂的电化学行为.
准确的循环寿命估计对于开发先进的电池管理系统至关重要.

研究的目的:

开发和验证用于预测金属可充电电池循环寿命的机器学习 (ML) 模型.
探索从电池运行过程中提取的各种电化学特征的实用性,用于周期寿命预测.
确定影响电池退化和循环寿命的关键特征.

主要方法:

从电池操作的放电,充电和放松阶段提取的特征.
采用机器学习算法进行周期寿命预测,包括特征选择.
利用统计分析来确定特征的重要性和模型性能.

主要成果:

性能最好的ML模型实现了0.89.8的确定系数 (R2).
特性重要性分析确定了最小排放能力差异 (Log((((ΔDQ100-10((V)) 的对数作为最关键的预测特征.
该模型在未见的数据上显示了6.6%的低测试误差,表明了稳定性.

结论:

机器学习为预测先进金属电池的周期寿命提供了一种强大而准确的方法.
开发的模型提供了一种可靠的方法来预测电池的寿命,即使使用复杂的电极材料.
这项工作突出了基于机器学习的洞察力,用于增强电池管理系统和能源存储技术的潜力.

关键词:

金属阳极是一种金属阳极.电池电池电池电池电池电池生命周期生命周期生命周期功能功能功能功能功能功能功能机器学习模型机器学习模型

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