基于图神经网络的锂离子电池健康状态估计：利用部分放电曲线
Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve
AI总结：
本文提出利用Matrix Profile自动选择放电电压片段并结合图卷积网络捕捉电池循环间时空退化动态，在公开数据集上实现均方根误差小于1%的SOH估计。
摘要：
数据驱动方法在锂离子电池健康状态（SOH）估计中获得了广泛关注。准确的SOH估计需要与退化相关的特征以及训练集与测试集之间统计分布的对齐。然而，当前研究往往忽视这些需求，并依赖任意的电压片段选择。为应对这些挑战，本文提出了一种创新方法，利用图卷积网络（GCN）挖掘时空退化动态。我们的方法使用Matrix Profile异常检测算法系统地选择放电电压片段，无需人工选择并防止信息丢失。这些选定的片段构成集成到基于GCN的SOH估计模型中的基础结构，能够捕捉循环间动态并缓解离线训练数据与在线测试数据之间的统计分布不一致。在一个广泛认可的公开数据集上的验证表明，我们的方法实现了精确的SOH估计，均方根误差小于1%。
英文摘要：
Data-driven methods have gained extensive attention in estimating the state of health (SOH) of lithium-ion batteries. Accurate SOH estimation requires degradation-relevant features and alignment of statistical distributions between training and testing datasets. However, current research often overlooks these needs and relies on arbitrary voltage segment selection. To address these challenges, this paper introduces an innovative approach leveraging spatio-temporal degradation dynamics via graph convolutional networks (GCNs). Our method systematically selects discharge voltage segments using the Matrix Profile anomaly detection algorithm, eliminating the need for manual selection and preventing information loss. These selected segments form a fundamental structure integrated into the GCN-based SOH estimation model, capturing inter-cycle dynamics and mitigating statistical distribution incongruities between offline training and online testing data. Validation with a widely accepted open-source dataset demonstrates that our method achieves precise SOH estimation, with a root mean squared error of less than 1%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
