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

S. Thiede; A. Turetskyy; A. Kwade; S. Kara; C. Herrmann

doi:10.1016/j.cirp.2019.04.066

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

S. Thiede, A. Turetskyy, A. Kwade, S. Kara, C. Herrmann

Research output: Contribution to journal › Article › Academic › peer-review

87 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	463-466
Journal	CIRP Annals
Volume	68
Issue number	1
DOIs	https://doi.org/10.1016/j.cirp.2019.04.066
Publication status	Published - 2019
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.cirp.2019.04.066

Cite this

@article{adf3778415cf4710be02f8c4f9ba32ad,

title = "Data mining in battery production chains towards multi-criterial quality prediction",

abstract = "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.",

author = "S. Thiede and A. Turetskyy and A. Kwade and S. Kara and C. Herrmann",

year = "2019",

doi = "10.1016/j.cirp.2019.04.066",

language = "English",

volume = "68",

pages = "463--466",

journal = "CIRP Annals",

issn = "0007-8506",

publisher = "Elsevier Inc.",

number = "1",

}

TY - JOUR

T1 - Data mining in battery production chains towards multi-criterial quality prediction

AU - Thiede, S.

AU - Turetskyy, A.

AU - Kwade, A.

AU - Kara, S.

AU - Herrmann, C.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85064905207&partnerID=MN8TOARS

U2 - 10.1016/j.cirp.2019.04.066

DO - 10.1016/j.cirp.2019.04.066

M3 - Article

SN - 0007-8506

VL - 68

SP - 463

EP - 466

JO - CIRP Annals

JF - CIRP Annals

IS - 1

ER -

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

Abstract

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this