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离子电池的SOH估计使用放松时间的分布和功能优化的多层感知器

Fang Wang¹, Shiqiang Liu¹, Shiqin Chen²

¹China Automotive Technology & Research Center Co. Ltd, Tianjin 300300, China.

|October 7, 2025

概括

使用RQ元件的新电化学阻抗光谱 (EIS) 方法改善了电池健康监测. 这种方法通过减少参数灵敏度和特征冗余性来增强健康状况 (SOH) 估计.

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
数据科学数据科学数据科学

背景情况:

电化学阻抗光谱 (EIS) 提供了有关电池退化的宝贵数据.
目前的放松时间分布 (DRT) 方法与模两可的特征和参数选择作斗争.

研究的目的:

开发一个强大的DRT方法用于电池退化分析.
通过改进的阻抗式功能来增强健康状况 (SOH) 估计.

主要方法:

提出了一种新的DRT方法,利用RQ元素来降低参数灵敏度.
采用特征选择算法来优化SOH估计的信息峰值.
利用多层感知子 (MLP) 模型基于选定的特征进行SOH预测.

主要成果:

RQ-DRT方法显示出增强的稳定性和减少提取特征的模糊性.
优化的功能组合显著改善了SOH估计性能.
验证了对不同离子电池化学和充电状态的方法.

结论:

RQ-DRT方法为电池退化分析提供了一种实用且稳定的方法.
将基于阻抗的时间尺度分析与机器学习相结合,为电池管理系统 (BMS) 提供了可行的途径.
这项工作提高了电池中SOH监测的可靠性.

关键词:

应用计算应用计算的应用能源工程能源工程是指能源工程.材料科学是一种材料科学.

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