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使用数据驱动机器学习预测离子电池的循环寿命,使用三极阴极材料

Long Li¹, Pengfei Yue¹, Chongnian Tang²

¹Inner Mongolia Power Group Co. Ltd., Hohhot 010010, China.

|November 10, 2025

概括

机器学习模型准确地预测了离子电池寿命. XGBoost在预测三元阴极电池剩余使用寿命 (RUL) 时实现了11.8%的错误,这对于电动汽车和电网存储至关重要.

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:

带有三元阴极的离子电池对于电动汽车和电网存储至关重要,因为它们的能量和功率密度很高.
预测容量衰减和剩余使用寿命 (RUL) 对电池性能管理至关重要.

研究的目的:

开发和评估机器学习模型,用于预测三元阴极离子电池的RUL.
通过使用不同数量的初始循环数据来评估模型准确性.

主要方法:

使用了弹性网,随机森林和XGBoost机器学习算法.
在公开可用的离子电池循环数据上训练模型.
根据平均绝对误差 (MAE) 或类似指标评估预测准确性.

主要成果:

在使用前100个数据周期时,XGBoost模型以11.8%的误差实现了最高的预测准确度.
在只训练前30个周期时,预测误差增加到17.0%.
所有模型都显示了RUL预测的潜力,性能根据数据量而异.

结论:

机器学习,特别是XGBoost,为准确的电池RUL预测提供了一个有希望的方法.
早期的循环数据可能足以进行可靠的RUL估计.
准确的RUL预测对电动汽车和电网存储系统的维护有重大影响.

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