用图神经网络分析电池放电曲线，误差低于1%。
Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve
- 用矩阵轮廓算法自动选放电段，避免人工选择偏差
- 结合图卷积网络捕捉电池老化动态，误差低于1%
- 适合做电池健康度估计的工程师和研究者
数据驱动方法在锂离子电池健康状态（SOH）估计中受到广泛关注。准确估计SOH需要提取退化相关特征，并确保训练与测试数据的统计分布一致。然而，现有研究常忽视这些需求，依赖随意选取的电压段。为此，本文提出一种新方法，利用图卷积网络（GCN）建模时空退化动态。通过矩阵轮廓异常检测算法系统性地选择放电电压段，避免人工干预与信息丢失。所选段落构成基础结构，嵌入GCN模型，捕捉循环间动态变化，缓解离线训练与在线测试数据间的分布不一致性。在公开标准数据集上的验证表明，该方法实现高精度SOH估计，均方根误差小于1%。
原文摘要 · Abstract (English)
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%.
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