Automatic method for the estimation of li-ion degradation test sample sizesrequired to understand cell-to-cell variability  

作　　者：Calum Strange Michael Allerhand Philipp Dechent Gonçalo dos Reis 

机构地区：[1]School of Mathematics,University of Edinburgh,The Kings buildings,Edinburgh,EH93JF,Scotland,United Kingdom [2]Institute for Power Electronics and Electrical Drives(ISEA),RWTH Aachen University,Aachen,Germany [3]Centro de Matematica e Aplicacoes(CMA),Faculdade de Ciencias e Tecnologia,Campus da Caparica,Caparica,2829-516,Portugal

出　　处：《Energy and AI》2022年第3期139-147,共9页能源与人工智能（英文）

基　　金：funded by an industry-academia collaborative grant EPSRC EP/R511687/1 awarded by Engineering and Physical Sciences Research Council(EPSRC)&University of Edinburgh United Kingdom program Impact Acceleration Account(IAA).G.dos Reis acknowledges support from the Fundaçao para a Ciencia e a Tecnologia(Portuguese Foundation for Science and Technology)Portugal through the project UIDB/00297/2020 and UIDP/00297/2020(Center for Mathematics and Applications,CMA/FCT/UNL Portugal);P.Dechent was supported by Bundesministerium für Bildung und Forschung Germany(BMBF 03XP0302C).

摘　　要：The testing of battery cells is a long and expensive process, and hence understanding how large a test set needsto be is very useful. This work proposes an automated methodology to estimate the smallest sample size ofcells required to capture the cell-to-cell variability seen in a larger population. We define cell-to-cell variationbased on the slopes of a linear regression model applied to capacity fade curves. Our methodology determinesa sample size which estimates this variability within user specified requirements on precision and confidence.The sample size is found using the distributional properties of the slopes under a normality assumption, andan implementation of the approach is available on GitHub.For the five datasets in the study, we find that a sample size of 8–10 cells (at a prespecified precision andconfidence) captures the cell-to-cell variability of the larger datasets. We show that prior testing knowledge canbe leveraged with machine learning models to operationally optimise the design of new cell-testing, leadingup to a 75% reduction in experimental costs.

关 键 词：Battery Testing LITHIUM-ION Degradation STATISTICS Manufacturing Machine learning 

分 类 号：TM91[电气工程—电力电子与电力传动]