利用可解释深度学习和部分充电数据诊断复合电池电极的健康状况

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-02-27 DOI:10.1016/j.egyai.2024.100352
Haijun Ruan , Niall Kirkaldy , Gregory J. Offer , Billy Wu
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引用次数: 0

摘要

使用石墨和硅复合阳极的锂离子电池越来越多。然而,由于电极的混合性质,它们的降解途径非常复杂,石墨和硅的降解速度各不相同。在此,我们开发了一种深度学习健康诊断框架,利用部分充电数据快速量化和区分复合阳极中石墨和硅的不同降解率。利用合成数据训练的卷积神经网络(CNN)使用实验性部分充电数据来诊断测试电池的电极级健康状况,误差小于 3.1%(相当于活性材料损耗达到 75%)。对不同降解模式下的容量-电压曲线进行了灵敏度分析,从而为使用部分充电数据进行诊断提供了一个物理意义上的电压窗口。通过使用梯度加权类激活映射方法,我们对这些 CNN 的工作原理提供了可解释的见解;突出了它们最敏感的电压曲线区域。通过在数据中引入噪声验证了鲁棒性,噪声水平低于 10 mV 时对诊断准确性没有明显的负面影响,从而突出了深度学习方法在真实世界条件下诊断锂离子电池性能的潜力。本文介绍的框架可推广到其他电池形式和化学物质,为传统的单一材料电极以及更具挑战性的复合电极提供稳健且可解释的电池诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Diagnosing health in composite battery electrodes with explainable deep learning and partial charging data

Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work; highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.

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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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