Top

Production Engineering

02-05-2024 | Original Paper

Coating process control in lithium-ion battery manufacturing using cumulative sum charts

Authors: Min-Chang Liu, Fang-Rong Hsu, Chua-Huang Huang

Published in: Production Engineering

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The coating process in lithium-ion battery manufacturing is designed to distribute stirred slurry on substrates. The coating results have a significant effect on the performance of lithium-ion batteries. A well-controlled coating process can avoid material wastage in manufacturing and improve the safety of lithium-ion batteries. Studies have focused on factors that influence coating results. However, no systematic research has been conducted on the control of the coating process. In this paper, we propose an inexpensive but efficient approach to monitoring the results of slurry coating. A data measurement system, combined with cumulative sum charts and complex event processing, were developed to monitor the coating process. As part of our research, we utilized a cumulative sum chart to detect small changes in coating that could not be detected easily. Additionally, anomalies in the process were detected using complex event processing. Our work was applied in a real-world setting, and the experimental results showed that it was effective in detecting small changes in the slurry coating. Our research can be able to contribute to the development of a solution for the process control of slurry coating in the manufacture of lithium-ion batteries as well as bridge the gap between research and industry.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Antony J, McDermott O, Sony M (2021) Quality 4.0 conceptualisation and theoretical understanding: a global exploratory qualitative study. Tqm J 34:1169–1188. https://doi.org/10.1108/tqm-07-2021-0215CrossRef

Chan L-Y, Xie M, Goh TN (2000) Cumulative quantity control charts for monitoring production processes. Int J Prod Res 38:397–408. https://doi.org/10.1080/002075400189482CrossRef

Chiarini A (2020) Industry 4.0, quality management and TQM world. A systematic literature review and a proposed agenda for further research. Tqm J 32:603–616. https://doi.org/10.1108/tqm-04-2020-0082CrossRef

Coffi J-R, Marsala C, Museux N (2011) Hybrid Learning system for adaptive complex event processing. In: Lecture Notes in Computer Science. pp 260–271

Cohen Y, Faccio M, Pilati F, Yao X (2019) Design and management of digital manufacturing and assembly systems in the Industry 4.0 era. Int J Adv Manuf Technol 105:3565–3577. https://doi.org/10.1007/s00170-019-04595-0CrossRef

Cunha RP, Lombardo T, Primo EN, Franco AA (2019) Artificial intelligence investigation of NMC cathode manufacturing parameters interdependencies. Batter Supercaps 3:60–67. https://doi.org/10.1002/batt.201900135CrossRef

Dao PB (2021) A CUSUM-based approach for condition monitoring and fault diagnosis of wind turbines. Energies 14:3236. https://doi.org/10.3390/en14113236CrossRef

Duquesnoy M, Liu C, Dominguez DZ, Kumar V, Ayerbe E, Franco AA (2023) Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations. Energy Storag Mater 56:50–61. https://doi.org/10.1016/j.ensm.2022.12.040CrossRef

Flynn T, Yoo S (2019) Change Detection with the Kernel Cumulative Sum Algorithm. The Proceeding of 58th Conference on Decision and Control. https://doi.org/10.1109/cdc40024.2019.9029854

10.

Garcia HE, Valles A, Sanchez J, Noriega S, Dominguez GA (2016) Statistical equation modeling analysis for industrial projects, designing for critical factors and latent variables: quality, cost, time, and success. Int J Adv Manuf Technol 88:767–779. https://doi.org/10.1007/s00170-016-8675-4CrossRef

11.

Hua Y, Zhou S, Cui H, Liu X, Zhang C, Xingwu X, Ling H, Yang S (2020) A comprehensive review on inconsistency and equalization technology of lithium-ion battery for electric vehicles. Int J Energy Res 44:11059–11087. https://doi.org/10.1002/er.5683CrossRef

12.

Kumberg J, Muller M, Diehm R, Spiegel S, Wachsmann C, Bauer W, Scharfer P, Schabel W (2019) Drying of Lithium-Ion battery anodes for use in High-Energy cells: influence of electrode thickness on drying time, adhesion, and crack formation. Energy Technol. https://doi.org/10.1002/ente.201900722CrossRef

13.

Lang J, Capik Z (2014) Prediction based on hybrid method in complex event processing. In: Proceeding of IEEE 12th International Symposium on Applied Machine Intelligence and Informatics. https://doi.org/10.1109/sami.2014.6822430

14.

Liu K, Niri MF, Apachitei G, Lain M, Greenwood D, Marco J (2022) Interpretable machine learning for battery capacities prediction and coating parameters analysis. Control Eng Pract 124:105202. https://doi.org/10.1016/j.conengprac.2022.105202CrossRef

15.

Liu Y, Zhang R, Wang J, Wang Y (2021) Current and future lithium-ion battery manufacturing. iScience 24:102332. https://doi.org/10.1016/j.isci.2021.102332CrossRef

16.

Nawaz M, Maulud AS, Zabiri H, Taqvi SAA, Idris AK (2021) Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework. Chin J Chem Eng 29:253–265. https://doi.org/10.1016/j.cjche.2020.08.035CrossRef

17.

Qiu P, Li W, Li J (2019) A new process control chart for monitoring short-range serially correlated data. Technometrics 62:71–83. https://doi.org/10.1080/00401706.2018.1562988MathSciNetCrossRef

18.

Rahali D, Castagliola P, Taleb H, Khoo MBC (2019) Evaluation of Shewhart time-between-events-and-amplitude control charts for several distributions. Qual Eng 31:240–254. https://doi.org/10.1080/08982112.2018.1479036CrossRef

19.

Raza MA, Nawaz T, Aslam M (2018) On designing CUSUM charts using ratio-type estimators for monitoring the location of normal processes. Sci Iran. https://doi.org/10.24200/sci.2018.50429.1688CrossRef

20.

Regier P, Briceno HO, Boyer JN (2019) Analyzing and comparing complex environmental time series using a cumulative sums approach. MethodsX 6:779–787. https://doi.org/10.1016/j.mex.2019.03.014CrossRef

21.

Sindhu TN, Riaz M, Aslam M, Ahmed Z (2016) A Study of Cumulative Quantity Control Chart for a Mixture of Rayleigh Model under a Bayesian Framework. Revista Colombiana De Estadistica 39:185. https://doi.org/10.15446/rce.v39n2.58915MathSciNetCrossRef

22.

Sader S, Husti I, Daroczi M (2021) A review of quality 4.0: definitions, features, technologies, applications, and challenges. Total Qual Manag Bus Excell 33:1164–1182. https://doi.org/10.1080/14783363.2021.1944082CrossRef

23.

Saraka RM, Morelly SL, Tang MH, Alvarez NJ (2020) Correlating processing conditions to Short- and Long-Range Order in coating and drying Lithium-Ion batteries. ACS Appl Energy Mater 3:11681–11689. https://doi.org/10.1021/acsaem.0c01305CrossRef

24.

Schnell J, Nentwich C, Endres F, Kollenda A, Distel F, Knoche T, Reinhart G (2019) Data mining in lithium-ion battery cell production. J Power Sourc 413:360–366. https://doi.org/10.1016/j.jpowsour.2018.12.062CrossRef

25.

Schmidt O, Thomitzek M, Roder F, Thiede S, Herrmann C, Krewer U (2020) Modeling the impact of manufacturing uncertainties on Lithium-Ion batteries. J Electrochem Soc 167:060501. https://doi.org/10.1149/1945-7111/ab798aCrossRef

26.

Senoner J, Netland TH, Feuerriegel S (2022) Using explainable artificial intelligence to improve process quality: evidence from semiconductor manufacturing. Manag Sci 68:5704–5723. https://doi.org/10.1287/mnsc.2021.4190CrossRef

27.

Shafiq MZ, Thakre KS, Krishna KR, Robert NJ, Kuruppath A, Kumar D (2023) Continuous quality control evaluation during manufacturing using supervised learning algorithm for Industry 4.0. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-023-10847-xCrossRef

28.

Wanner J, Wissuchek C, Janiesch C (2020) Machine learning and complex event processing. Enterprise Modelling and Information Systems Architectures (EMISAJ) 15. https://doi.org/10.18417/emisa.15.1

29.

Zhan FB, Martinez A, Rai N, Mcconnell R, Swan M, Bhaduri M, Zhan J, Gewali L, Oh P (2019) Beyond cumulative sum charting in Non-Stationarity detection and estimation. IEEE Access 7:140860–140874. https://doi.org/10.1109/access.2019.2943446CrossRef

30.

Zheng L, Hatchard TD, Obrovac MN (2019) A high-quality mechanofusion coating for enhancing lithium-ion battery cathode material performance. MRS Commun 9:245–250. https://doi.org/10.1557/mrc.2018.209CrossRef

31.

Zonnenshain A, Kenett RS (2020) Quality 4.0-the challenging future of quality engineering. Qual Eng 32:614–626. https://doi.org/10.1080/08982112.2019.1706744CrossRef

32.

Hawkins DM, Olwell DH (1998) Cumulative sum charts and charting for quality improvement

33.

Escobar CA, McGovern ME, Morales-Menendez R (2021) Quality 4.0: a review of big data challenges in manufacturing. J Intell Manuf 32:2319–2334. https://doi.org/10.1007/s10845-021-01765-4CrossRef

34.

Saeed N, Abu-Shawiesh MOA (2021) A trimmed EWMA control charts for non-normal processes. Prod Eng Res Dev 15:545–561. https://doi.org/10.1007/s11740-021-01039-1CrossRef

Title: Coating process control in lithium-ion battery manufacturing using cumulative sum charts
Authors: Min-Chang Liu
Fang-Rong Hsu
Chua-Huang Huang
Publication date: 02-05-2024
Publisher: Springer Berlin Heidelberg
Published in: Production Engineering
Print ISSN: 0944-6524
Electronic ISSN: 1863-7353
DOI: https://doi.org/10.1007/s11740-024-01281-3

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Premium Partners