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域知识导向的机器学习框架用于估计离子电池的健康状况

Andrea Lanubile¹, Pietro Bosoni¹, Gabriele Pozzato¹

¹Energy Science & Engineering, Stanford University, Stanford, CA, USA.

Communications engineering

|November 12, 2024

概括

我们为电动汽车电池开发了五个新的健康指标,使用机器学习来准确估计健康状况. 这种方法即使使用不完整的数据也可行,改善了电池管理.

科学领域:

电化学能量储存系统电化学能量储存系统
电池管理系统电池管理系统
机器学习在工程中的应用.

背景情况:

准确的电池健康状况 (SoH) 估计对于电动汽车 (EV) 电池管理至关重要.
当前的方法经常与现实世界的操作数据和缺少的信息作斗争.
了解电池退化机制 (能量和功率衰减) 是可靠的SoH评估的关键.

研究的目的:

为电动汽车电池提出新的,在线可提取的健康指标.
开发一个机器学习模型,使用这些指标来准确估计SoH.
为了在各种操作条件下实现实时电池退化监控.

主要方法:

从在线,现实世界的EV运营数据中提取了五个新的健康指标.
开发基于机器学习的方法,特别是线性回归,用于SoH估计.
在EV条件下使用5个老旧电池电池的实验数据进行验证.

主要成果:

拟议的指标提供了对电池能量和功率衰减的物理见解.
即使部分缺少数据,也实现了准确的容量估计.
使用功率自关联和基于能源的特征的模型产生了1.5%至2.5%的最大绝对百分比误差.

结论:

开发的健康指标和机器学习模型为实时EV电池SoH估计提供了强大的方法.
该方法通过早期检测退化,促进了有效的电池管理.
该方法处理不完整数据和各种操作条件的能力提高了其实际适用性.

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