A Conceptual Framework towards Data-Driven Models in Electrode Production of Lithium-Ion Battery Cells

Sajedeh Haghi; Hans Christoph Töpper; Florian J. Günter; Gunther Reinhart

doi:10.1016/j.procir.2021.11.194

A Conceptual Framework towards Data-Driven Models in Electrode Production of Lithium-Ion Battery Cells

Sajedeh Haghi, Hans Christoph Töpper, Florian J. Günter, Gunther Reinhart

Chair of Sustainable Production

Research output: Contribution to journal › Conference article › peer-review

5 Scopus citations

Abstract

The introduction of Industry 4.0 and its driving technologies enables companies to collect data from production systems. The increasing amount of data propels the development of data-driven methods for modeling and predictive analytics. The adoption of these models in battery cell production has been gaining attention over recent years, as the process chain, especially electrode production consists of complex and strongly interrelated process steps. This paper outlines a systematic approach for developing data-driven models in electrode production, including the topic of digitalization and identification of data points. The framework supports practitioners towards digitalization and analysis of interdependencies in electrode production.

Original language	English
Pages (from-to)	1155-1160
Number of pages	6
Journal	Procedia CIRP
Volume	104
DOIs	https://doi.org/10.1016/j.procir.2021.11.194
State	Published - 2021
Event	54th CIRP Conference on Manufacturing Ssystems, CMS 2021 - Patras, Greece Duration: 22 Sep 2021 → 24 Sep 2021

Keywords

Battery Cell Production
Data Analytics
Data-driven Models
Electrode Production

UN SDGs

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

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10.1016/j.procir.2021.11.194

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title = "A Conceptual Framework towards Data-Driven Models in Electrode Production of Lithium-Ion Battery Cells",

abstract = "The introduction of Industry 4.0 and its driving technologies enables companies to collect data from production systems. The increasing amount of data propels the development of data-driven methods for modeling and predictive analytics. The adoption of these models in battery cell production has been gaining attention over recent years, as the process chain, especially electrode production consists of complex and strongly interrelated process steps. This paper outlines a systematic approach for developing data-driven models in electrode production, including the topic of digitalization and identification of data points. The framework supports practitioners towards digitalization and analysis of interdependencies in electrode production.",

keywords = "Battery Cell Production, Data Analytics, Data-driven Models, Electrode Production",

author = "Sajedeh Haghi and T{\"o}pper, {Hans Christoph} and G{\"u}nter, {Florian J.} and Gunther Reinhart",

year = "2021",

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journal = "Procedia CIRP",

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T1 - A Conceptual Framework towards Data-Driven Models in Electrode Production of Lithium-Ion Battery Cells

AU - Haghi, Sajedeh

AU - Töpper, Hans Christoph

AU - Günter, Florian J.

AU - Reinhart, Gunther

PY - 2021

Y1 - 2021

N2 - The introduction of Industry 4.0 and its driving technologies enables companies to collect data from production systems. The increasing amount of data propels the development of data-driven methods for modeling and predictive analytics. The adoption of these models in battery cell production has been gaining attention over recent years, as the process chain, especially electrode production consists of complex and strongly interrelated process steps. This paper outlines a systematic approach for developing data-driven models in electrode production, including the topic of digitalization and identification of data points. The framework supports practitioners towards digitalization and analysis of interdependencies in electrode production.

AB - The introduction of Industry 4.0 and its driving technologies enables companies to collect data from production systems. The increasing amount of data propels the development of data-driven methods for modeling and predictive analytics. The adoption of these models in battery cell production has been gaining attention over recent years, as the process chain, especially electrode production consists of complex and strongly interrelated process steps. This paper outlines a systematic approach for developing data-driven models in electrode production, including the topic of digitalization and identification of data points. The framework supports practitioners towards digitalization and analysis of interdependencies in electrode production.

KW - Battery Cell Production

KW - Data Analytics

KW - Data-driven Models

KW - Electrode Production

UR - http://www.scopus.com/inward/record.url?scp=85121588352&partnerID=8YFLogxK

U2 - 10.1016/j.procir.2021.11.194

DO - 10.1016/j.procir.2021.11.194

M3 - Conference article

AN - SCOPUS:85121588352

SN - 2212-8271

VL - 104

SP - 1155

EP - 1160

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 54th CIRP Conference on Manufacturing Ssystems, CMS 2021

Y2 - 22 September 2021 through 24 September 2021

ER -

A Conceptual Framework towards Data-Driven Models in Electrode Production of Lithium-Ion Battery Cells

Abstract

Keywords

UN SDGs

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