Data mining in lithium-ion battery cell production
Data mining methods are used to analyze and improve production processes in a lithium-ion cell manufacturing line. The CRISP-DM methodology is applied to the data captured during the manufacturing processes. Key goals include the identification of process dependencies and key quality drivers as well...
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| Published in: | Journal of power sources Vol. 413; pp. 360 - 366 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
15.02.2019
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| ISSN: | 0378-7753, 1873-2755 |
| Online Access: | Get full text |
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| Abstract | Data mining methods are used to analyze and improve production processes in a lithium-ion cell manufacturing line. The CRISP-DM methodology is applied to the data captured during the manufacturing processes. Key goals include the identification of process dependencies and key quality drivers as well as the prediction of the product quality before the cumbersome and costly formation and aging procedure. Several Data mining methods, such as Generalized Linear Model (GLM), Artificial Neural Networks (ANN), Support Vector Regression (SVR), Decision Trees (DT), Random Forest (RF), and Gradient Boosted Trees (GBT) are compared and evaluated. Best results are yielded by an application of GLM, RF, and GBT for prediction of battery cell capacity before the expensive formation process. Key quality drivers identified are the electrode fabrication processes, as well as the electrolyte filling process during cell assembly. This is, to our knowledge, the first time data from a real battery production line has been systematically processed and analyzed along the whole process chain. The results of this paper can assist to manufacture better batteries and to reduce costs of lithium-ion cells by providing a systematic procedure for data acquisition and by lowering scrap rates during production.
[Display omitted]
•Data mining approaches were applied to a real battery production line.•A systematic procedure for data acquisition, processing, and analysis is given.•Electrode fabrication and electrolyte filling are identified as key quality drivers.•The results can help to decrease battery production cost by reducing scrap rates. |
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| AbstractList | Data mining methods are used to analyze and improve production processes in a lithium-ion cell manufacturing line. The CRISP-DM methodology is applied to the data captured during the manufacturing processes. Key goals include the identification of process dependencies and key quality drivers as well as the prediction of the product quality before the cumbersome and costly formation and aging procedure. Several Data mining methods, such as Generalized Linear Model (GLM), Artificial Neural Networks (ANN), Support Vector Regression (SVR), Decision Trees (DT), Random Forest (RF), and Gradient Boosted Trees (GBT) are compared and evaluated. Best results are yielded by an application of GLM, RF, and GBT for prediction of battery cell capacity before the expensive formation process. Key quality drivers identified are the electrode fabrication processes, as well as the electrolyte filling process during cell assembly. This is, to our knowledge, the first time data from a real battery production line has been systematically processed and analyzed along the whole process chain. The results of this paper can assist to manufacture better batteries and to reduce costs of lithium-ion cells by providing a systematic procedure for data acquisition and by lowering scrap rates during production.
[Display omitted]
•Data mining approaches were applied to a real battery production line.•A systematic procedure for data acquisition, processing, and analysis is given.•Electrode fabrication and electrolyte filling are identified as key quality drivers.•The results can help to decrease battery production cost by reducing scrap rates. |
| Author | Schnell, Joscha Distel, Fabian Nentwich, Corbinian Reinhart, Gunther Endres, Florian Knoche, Thomas Kollenda, Anna |
| Author_xml | – sequence: 1 givenname: Joscha surname: Schnell fullname: Schnell, Joscha email: Joscha.Schnell@iwb.mw.tum.de – sequence: 2 givenname: Corbinian surname: Nentwich fullname: Nentwich, Corbinian – sequence: 3 givenname: Florian surname: Endres fullname: Endres, Florian – sequence: 4 givenname: Anna surname: Kollenda fullname: Kollenda, Anna – sequence: 5 givenname: Fabian surname: Distel fullname: Distel, Fabian – sequence: 6 givenname: Thomas surname: Knoche fullname: Knoche, Thomas – sequence: 7 givenname: Gunther surname: Reinhart fullname: Reinhart, Gunther |
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| Cites_doi | 10.1016/j.jpowsour.2018.01.081 10.1023/A:1012487302797 10.1016/j.procir.2016.11.101 10.1016/j.procir.2016.11.098 10.1016/j.jpowsour.2014.11.019 10.1016/j.cirp.2012.03.101 10.1016/j.jpowsour.2016.09.037 10.1016/j.procir.2014.05.026 10.1002/aic.10315 10.4028/www.scientific.net/AMR.907.365 10.1038/s41560-018-0130-3 10.1016/j.procir.2015.12.044 10.4028/www.scientific.net/AMR.1140.304 10.1017/S0269888910000032 10.1016/j.jbiotec.2010.04.005 10.1016/j.ijpe.2006.05.015 |
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| References | Jemwa, Aldrich (bib15) 2005; 51 Cao, Lim, Zhou, Ho, Cheung, Motoda (bib24) 2015 Kantardzic (bib22) 2011 Knoche, Zinth, Schulz, Schnell, Gilles, Reinhart (bib8) 2016; 331 Mariscal, Marbán, Fernández (bib18) 2010; 25 Günther, Billot, Schuster, Schnell, Spingler, Gasteiger (bib6) 2016; 1140 Chapman (bib17) 2000 Cheng, Liu (bib13) 2015; 132 Kwade, Haselrieder, Leithoff, Modlinger, Dietrich, Droeder (bib1) 2018; 3 Schnell, Reinhart (bib9) 2016; 57 Huber, Tammer, Krotil, Waidmann, Hao, Seidel, Reinhart (bib16) 2016; 57 Westermeier, Reinhart, Zeilinger (bib2) 2013 Kurfer, Westermeier, Tammer, Reinhart (bib20) 2012; 61 Evans, Boreland (bib12) 2015 Weydanz, Reisenweber, Gottschalk, Schulz, Knoche, Reinhart, Masuch, Franke, Gilles (bib26) 2018; 380 Hsu, Chien (bib11) 2007; 107 accessed 9 October 2018. Westermeier, Reinhart, Steber (bib5) 2014; 20 Reinhart, Kurfer, Westermeier, Zeilinger (bib21) 2014; 907 Knoche, Surek, Reinhart (bib7) 2016; 41 Charaniya, Le, Rangwala, Mills, Johnson, Karypis, Hu (bib14) 2010; 147 Dinger, Martin, Mosquet, Rabl, Rizoulis, Russo, Sticher (bib3) 2010 (bib10) 2000 Wood, Li, Daniel (bib4) 2015; 275 RapidMiner Guyon, Weston, Barnhill, Vapnik (bib25) 2002; 46 (bib19) 2013 Charaniya (10.1016/j.jpowsour.2018.12.062_bib14) 2010; 147 Cao (10.1016/j.jpowsour.2018.12.062_bib24) 2015 Jemwa (10.1016/j.jpowsour.2018.12.062_bib15) 2005; 51 (10.1016/j.jpowsour.2018.12.062_bib19) 2013 10.1016/j.jpowsour.2018.12.062_bib23 Westermeier (10.1016/j.jpowsour.2018.12.062_bib5) 2014; 20 Dinger (10.1016/j.jpowsour.2018.12.062_bib3) 2010 Hsu (10.1016/j.jpowsour.2018.12.062_bib11) 2007; 107 Günther (10.1016/j.jpowsour.2018.12.062_bib6) 2016; 1140 Knoche (10.1016/j.jpowsour.2018.12.062_bib8) 2016; 331 Weydanz (10.1016/j.jpowsour.2018.12.062_bib26) 2018; 380 (10.1016/j.jpowsour.2018.12.062_bib10) 2000 Huber (10.1016/j.jpowsour.2018.12.062_bib16) 2016; 57 Reinhart (10.1016/j.jpowsour.2018.12.062_bib21) 2014; 907 Kwade (10.1016/j.jpowsour.2018.12.062_bib1) 2018; 3 Wood (10.1016/j.jpowsour.2018.12.062_bib4) 2015; 275 Kurfer (10.1016/j.jpowsour.2018.12.062_bib20) 2012; 61 Kantardzic (10.1016/j.jpowsour.2018.12.062_bib22) 2011 Cheng (10.1016/j.jpowsour.2018.12.062_bib13) 2015; 132 Chapman (10.1016/j.jpowsour.2018.12.062_bib17) 2000 Westermeier (10.1016/j.jpowsour.2018.12.062_bib2) 2013 Knoche (10.1016/j.jpowsour.2018.12.062_bib7) 2016; 41 Mariscal (10.1016/j.jpowsour.2018.12.062_bib18) 2010; 25 Schnell (10.1016/j.jpowsour.2018.12.062_bib9) 2016; 57 Evans (10.1016/j.jpowsour.2018.12.062_bib12) 2015 Guyon (10.1016/j.jpowsour.2018.12.062_bib25) 2002; 46 |
| References_xml | – volume: 61 start-page: 1 year: 2012 end-page: 4 ident: bib20 publication-title: CIRP Ann. – year: 2010 ident: bib3 article-title: The Boston Consulting Group – volume: 57 start-page: 585 year: 2016 end-page: 590 ident: bib16 publication-title: Procedia CIRP – volume: 51 start-page: 526 year: 2005 end-page: 543 ident: bib15 publication-title: AIChE J. – reference: RapidMiner, – volume: 132 year: 2015 ident: bib13 publication-title: J. Appl. Polym. Sci. – volume: 25 start-page: 137 year: 2010 end-page: 166 ident: bib18 publication-title: Knowl. Eng. Rev. – start-page: 1 year: 2015 end-page: 5 ident: bib12 article-title: IEEE 42nd Photovoltaic Specialist Conference (PVSC) – reference: , accessed 9 October 2018. – volume: 107 start-page: 88 year: 2007 end-page: 103 ident: bib11 publication-title: Int. J. Prod. Econ. – start-page: 3 year: 2013 end-page: 12 ident: bib19 publication-title: Future Trends in Production Engineering: Proceedings of the First Conference of the German Academic Society for Production Engineering (WGP), Berlin, Germany, 8th-9th June 2011 – start-page: 1 year: 2013 end-page: 10 ident: bib2 publication-title: 2013 3rd International Electric Drives Production Conference (EDPC) – volume: 57 start-page: 568 year: 2016 end-page: 573 ident: bib9 publication-title: Procedia CIRP – volume: 20 start-page: 13 year: 2014 end-page: 19 ident: bib5 publication-title: Procedia CIRP – volume: 275 start-page: 234 year: 2015 end-page: 242 ident: bib4 publication-title: J. Power Sources – volume: 1140 start-page: 304 year: 2016 end-page: 311 ident: bib6 publication-title: AMR – year: 2011 ident: bib22 article-title: Data Mining: Concepts, Models, Methods, and Algorithms – volume: 3 start-page: 290 year: 2018 end-page: 300 ident: bib1 publication-title: Nat. Energy – year: 2015 ident: bib24 article-title: Advances in Knowledge Discovery and Data Mining – year: 2000 ident: bib17 article-title: CRISP-DM 1.0: Step-by-step Data Mining Guide – volume: 41 start-page: 405 year: 2016 end-page: 410 ident: bib7 publication-title: Procedia CIRP – volume: 331 start-page: 267 year: 2016 end-page: 276 ident: bib8 publication-title: J. Power Sources – volume: 907 start-page: 365 year: 2014 end-page: 378 ident: bib21 publication-title: AMR – volume: 46 start-page: 389 year: 2002 end-page: 422 ident: bib25 publication-title: Mach. Learn. – volume: 380 start-page: 126 year: 2018 end-page: 134 ident: bib26 publication-title: J. Power Sources – volume: 147 start-page: 186 year: 2010 end-page: 197 ident: bib14 publication-title: J. Biotechnol. – start-page: 249 year: 2000 end-page: 252 ident: bib10 publication-title: Proceedings of ISSM2000. Ninth International Symposium on Semiconductor Manufacturing (IEEE Cat. No.00CH37130) – volume: 380 start-page: 126 year: 2018 ident: 10.1016/j.jpowsour.2018.12.062_bib26 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2018.01.081 – volume: 46 start-page: 389 year: 2002 ident: 10.1016/j.jpowsour.2018.12.062_bib25 publication-title: Mach. Learn. doi: 10.1023/A:1012487302797 – volume: 132 year: 2015 ident: 10.1016/j.jpowsour.2018.12.062_bib13 publication-title: J. Appl. Polym. Sci. – year: 2010 ident: 10.1016/j.jpowsour.2018.12.062_bib3 – start-page: 3 year: 2013 ident: 10.1016/j.jpowsour.2018.12.062_bib19 – year: 2015 ident: 10.1016/j.jpowsour.2018.12.062_bib24 – volume: 57 start-page: 585 year: 2016 ident: 10.1016/j.jpowsour.2018.12.062_bib16 publication-title: Procedia CIRP doi: 10.1016/j.procir.2016.11.101 – volume: 57 start-page: 568 year: 2016 ident: 10.1016/j.jpowsour.2018.12.062_bib9 publication-title: Procedia CIRP doi: 10.1016/j.procir.2016.11.098 – volume: 275 start-page: 234 year: 2015 ident: 10.1016/j.jpowsour.2018.12.062_bib4 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2014.11.019 – volume: 61 start-page: 1 year: 2012 ident: 10.1016/j.jpowsour.2018.12.062_bib20 publication-title: CIRP Ann. doi: 10.1016/j.cirp.2012.03.101 – start-page: 1 year: 2013 ident: 10.1016/j.jpowsour.2018.12.062_bib2 – ident: 10.1016/j.jpowsour.2018.12.062_bib23 – year: 2000 ident: 10.1016/j.jpowsour.2018.12.062_bib17 – volume: 331 start-page: 267 year: 2016 ident: 10.1016/j.jpowsour.2018.12.062_bib8 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2016.09.037 – year: 2011 ident: 10.1016/j.jpowsour.2018.12.062_bib22 – volume: 20 start-page: 13 year: 2014 ident: 10.1016/j.jpowsour.2018.12.062_bib5 publication-title: Procedia CIRP doi: 10.1016/j.procir.2014.05.026 – start-page: 249 year: 2000 ident: 10.1016/j.jpowsour.2018.12.062_bib10 – volume: 51 start-page: 526 year: 2005 ident: 10.1016/j.jpowsour.2018.12.062_bib15 publication-title: AIChE J. doi: 10.1002/aic.10315 – volume: 907 start-page: 365 year: 2014 ident: 10.1016/j.jpowsour.2018.12.062_bib21 publication-title: AMR doi: 10.4028/www.scientific.net/AMR.907.365 – volume: 3 start-page: 290 year: 2018 ident: 10.1016/j.jpowsour.2018.12.062_bib1 publication-title: Nat. Energy doi: 10.1038/s41560-018-0130-3 – volume: 41 start-page: 405 year: 2016 ident: 10.1016/j.jpowsour.2018.12.062_bib7 publication-title: Procedia CIRP doi: 10.1016/j.procir.2015.12.044 – volume: 1140 start-page: 304 year: 2016 ident: 10.1016/j.jpowsour.2018.12.062_bib6 publication-title: AMR doi: 10.4028/www.scientific.net/AMR.1140.304 – volume: 25 start-page: 137 year: 2010 ident: 10.1016/j.jpowsour.2018.12.062_bib18 publication-title: Knowl. Eng. Rev. doi: 10.1017/S0269888910000032 – start-page: 1 year: 2015 ident: 10.1016/j.jpowsour.2018.12.062_bib12 – volume: 147 start-page: 186 year: 2010 ident: 10.1016/j.jpowsour.2018.12.062_bib14 publication-title: J. Biotechnol. doi: 10.1016/j.jbiotec.2010.04.005 – volume: 107 start-page: 88 year: 2007 ident: 10.1016/j.jpowsour.2018.12.062_bib11 publication-title: Int. J. Prod. Econ. doi: 10.1016/j.ijpe.2006.05.015 |
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| SubjectTerms | Battery production Data mining Lithium-ion cell Process chain Quality management |
| Title | Data mining in lithium-ion battery cell production |
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