Data mining in battery production chains towards multi-criterial quality prediction

被引：58

作者：

Thiede, Sebastian ^{[1
,3
]}

Turetskyy, Artem ^{[1
,3
]}

Kwade, Arno ^{[2
,3
]}

Kara, Sami ^{[4
]}

Herrmann, Christoph ^{[1
,3
]}

机构：

[1] Tech Univ Carolo Wilhelmina Braunschweig, Inst Machine Tools & Prod Technol IWF, Chair Sustainable Mfg & Life Cycle Engn, Braunschweig, Germany

[2] Tech Univ Carolo Wilhelmina Braunschweig, Inst Particle Technol IPAT, Braunschweig, Germany

[3] Tech Univ Carolo Wilhelmina Braunschweig, BLB, Braunschweig, Germany

[4] Univ New South Wales, Sch Mech & Mfg Engn, Sustainable Mfg & Life Cycle Engn Res Grp, Sydney, NSW, Australia

来源：

CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2019年 / 68卷 / 01期

关键词：

Factory; Modelling; Data mining;

D O I：

10.1016/j.cirp.2019.04.066

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Battery production has become an increasingly important issue for industry e.g. due to the advent of electric cars and the greening of grids. The battery production chain is very interdisciplinary and consists of many specialised, innovative processes and numerous influencing factors. In contrast to more established sectors, processes and their interactions are not well understood yet. Thus, this paper presents a data mining approach for predicting different quality parameters of battery cells based on extensive data acquisition over the whole process chain. The results can be used to improve the planning and control of battery production. (C) 2019 Published by Elsevier Ltd on behalf of CIRP.

引用

页码：463 / 466

页数：4

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