Battery production design using multi-output machine learning models
作者： Sebastian Thiede , Christoph Herrmann , Artem Turetskyy , Jacob Wessel
DOI: 10.1016/J.ENSM.2021.03.002
关键词: Materials science 、 Energy storage 、 Scrap 、 Reliability engineering 、 Process (engineering) 、 Decision support system 、 Production (economics) 、 Battery (electricity) 、 Quality (business) 、 Final product
摘要: Abstract The lithium-ion battery (LiB) is a prominent energy storage technology playing an important role in the future of e-mobility and transformation sector. However, LiB cell manufacturing has still high production costs environmental impact, due to costly materials, process fluctuations with scrap rates, demands. A lack profound knowledge processes their influence on quality performance cells makes it difficult plan, control execute production. Therefore, systematic approach necessary establish in-depth understanding interlinkage products’ performance. This paper presents multi-output for design, based data-driven models predicting final product properties from intermediate features. given concept shows how can be deployed within framework cyber-physical system continuous improvement underlying model decision support
sci-hub.st 参考文章(59)
S. Hickel, DNS and LES of Two-Phase Flows with Cavitation arXiv: Fluid Dynamics. pp. 595- 604 ,(2015) , 10.1007/978-3-319-14448-1_75
Yangping Sheng, Christopher R. Fell, Yong Kyu Son, Bernhard M. Metz, Junwei Jiang, Benjamin C. Church, Effect of Calendering on Electrode Wettability in Lithium-Ion Batteries Frontiers in Energy Research. ,vol. 2, ,(2014) , 10.3389/FENRG.2014.00056
Werner Bauer, Dorit Nötzel, Valentin Wenzel, Hermann Nirschl, Influence of dry mixing and distribution of conductive additives in cathodes for lithium ion batteries Journal of Power Sources. ,vol. 288, pp. 359- 367 ,(2015) , 10.1016/J.JPOWSOUR.2015.04.081
Sang Gun Lee, Dong Hyup Jeon, Effect of electrode compression on the wettability of lithium-ion batteries Journal of Power Sources. ,vol. 265, pp. 363- 369 ,(2014) , 10.1016/J.JPOWSOUR.2014.04.127
Daniel Lieber, Marco Stolpe, Benedikt Konrad, Jochen Deuse, Katharina Morik, Quality Prediction in Interlinked Manufacturing Processes based on Supervised & Unsupervised Machine Learning Procedia CIRP. ,vol. 7, pp. 193- 198 ,(2013) , 10.1016/J.PROCIR.2013.05.033
Andrea Saltelli, Making best use of model evaluations to compute sensitivity indices Computer Physics Communications. ,vol. 145, pp. 280- 297 ,(2002) , 10.1016/S0010-4655(02)00280-1
Chang-Jun Bae, Can K. Erdonmez, John W. Halloran, Yet-Ming Chiang, Design of Battery Electrodes with Dual-Scale Porosity to Minimize Tortuosity and Maximize Performance Advanced Materials. ,vol. 25, pp. 1254- 1258 ,(2013) , 10.1002/ADMA.201204055
W. Haselrieder, S. Ivanov, H.Y. Tran, S. Theil, L. Froböse, B. Westphal, M. Wohlfahrt-Mehrens, A. Kwade, Influence of formulation method and related processes on structural, electrical and electrochemical properties of LMS/NCA-blend electrodes Progress in Solid State Chemistry. ,vol. 42, pp. 157- 174 ,(2014) , 10.1016/J.PROGSOLIDSTCHEM.2014.04.009
Andrea Saltelli, Paola Annoni, Ivano Azzini, Francesca Campolongo, Marco Ratto, Stefano Tarantola, Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index Computer Physics Communications. ,vol. 181, pp. 259- 270 ,(2010) , 10.1016/J.CPC.2009.09.018
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Andreas Müller, Joel Nothman, Gilles Louppe, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay, Scikit-learn: Machine Learning in Python Journal of Machine Learning Research. ,vol. 12, pp. 2825- 2830 ,(2011)