Unsupervised learning of charge-discharge cycles from various lithium-ion battery cells to visualize dataset characteristics and to interpret model performance

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Akihiro Yamashita , Sascha Berg , Egbert Figgemeier
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引用次数: 0

Abstract

Machine learning (ML) is a rapidly growing tool even in the lithium-ion battery (LIB) research field. To utilize this tool, more and more datasets have been published. However, applicability of a ML model to different information sources or various LIB cell types has not been well studied. In this paper, an unsupervised learning model called variational autoencoder (VAE) is evaluated with three datasets of charge-discharge cycles with different conditions. The model was first trained with a publicly available dataset of commercial cylindrical cells, and then evaluated with our private datasets of commercial pouch and hand-made coin cells. These cells used different chemistry and were tested with different cycle testers under different purposes, which induces various characteristics to each dataset. We report that researchers can recognise these characteristics with VAE to plan a proper data preprocessing. We also discuss about interpretability of a ML model.

Abstract Image

对各种锂离子电池的充放电循环进行无监督学习,以直观显示数据集特征并解释模型性能
即使在锂离子电池(LIB)研究领域,机器学习(ML)也是一种快速发展的工具。为了利用这一工具,已经发布了越来越多的数据集。然而,ML 模型对不同信息源或各种锂离子电池类型的适用性还没有得到很好的研究。本文利用三个不同条件下的充放电循环数据集,对一种名为变异自动编码器(VAE)的无监督学习模型进行了评估。该模型首先使用公开的商用圆柱形电池数据集进行训练,然后使用我们自己的商用袋装电池和手工制造的硬币电池数据集进行评估。这些电池使用了不同的化学成分,并在不同目的下使用不同的循环测试仪进行了测试,从而使每个数据集都具有不同的特征。我们的报告指出,研究人员可以利用 VAE 识别这些特征,从而制定适当的数据预处理计划。我们还讨论了 ML 模型的可解释性。
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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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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