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

作　　者：Akihiro Yamashita Sascha Berg Egbert Figgemeier 

机构地区：[1]Helmholtz Institute Münster:Ionics in Energy Storage(IMD-4/HI MS),Forschungszentrum Jülich,Jülich,Germany [2]Institute for Power Electronics and Electrical Drives(ISEA),RWTH Aachen University,Aachen,Germany [3]Jülich Aachen Research Alliance,JARA-Energy,Germany

出　　处：《Energy and AI》2024年第3期397-405,共9页能源与人工智能（英文）

基　　金：supported by the project“ZeDaBase-Batteriezelldatenbank”of the Initiative and Networking Fund of the Helmholtz Association(KW-BASF-6).

摘　　要：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.

关 键 词：Unsupervised learning Dimensionality reduction Inductive bias .Machine learning Variational autoencoder 

分 类 号：TM912[电气工程—电力电子与电力传动]