@article{StierXuGoldetal.2024, author = {Stier, Simon P. and Xu, Xukuan and Gold, Lukas and M{\"o}ckel, Michael}, title = {Ontology-Based Battery Production Dataspace and Its Interweaving with Artificial Intelligence-Empowered Data Analytics}, series = {Energy Technology}, volume = {2024}, journal = {Energy Technology}, number = {2301305}, doi = {https://doi.org/10.1002/ente.202301305}, pages = {1 -- 13}, year = {2024}, abstract = {Heart disease, also known as cardiovascular disease, encompasses a variety of heart conditions that can result in sudden death for many people. Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method's utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia.}, subject = {Batterie}, language = {en} } @article{ZanottoZapataDominguezAyerbeetal.2022, author = {Zanotto, Franco M. and Zapata Dominguez, Diana and Ayerbe, Elixabete and Boyano, Iker and Burmeister, Christine and Duquesnoy, Marc and Eisentraeger, Marlene and Florez Monta{\~n}o, Jonathan and Gallo-Bueno,, Alfonso and Gold, Lukas and Hall, Florian and Kaden, Nicolaj and Muerkens, Bernhard and Otaegui, Laida and Reynier, Yvan and Stier, Simon and Thomitzek, Matthias and Turetskyy, Artem and Vallin, Nicolas and Wessel, Jacob and Xu, Xukuan and Abbasov, Jeyhun and Franco, Alejandro A.}, title = {Data Specifications for Battery Manufacturing Digitalization: Current Status, Challenges, and Opportunities}, series = {Chemistry Europe - Batteries \& Supercaps}, volume = {2022}, journal = {Chemistry Europe - Batteries \& Supercaps}, number = {5/9}, doi = {https://doi.org/10.1002/batt.202200224}, pages = {1 -- 35}, year = {2022}, abstract = {Lithium-ion battery (LIB) manufacturing requires a pilot stage that optimizes its characteristics. However, this process is costly and time-consuming. One way to overcome this is to use a set of computational models that act as a digital twin of the pilot line, exchanging information in real-time that can be compared with measurements to correct parameters. Here we discuss the parameters involved in each step of LIB manufacturing, show available computational modeling approaches, and discuss details about practical implementation in terms of software. Then, we analyze these parameters regarding their criticality for modeling set-up and validation, measurement accuracy, and rapidity. Presenting this in an understandable format allows identifying missing aspects, remaining challenges, and opportunities for the emergence of pilot lines integrating digital twins. Finally, we present the challenges of managing the data produced by these models. As a snapshot of the state-of-the-art, this work is an initial step towards digitalizing battery manufacturing pilot lines, paving the way toward autonomous optimization.}, subject = {Batterie}, language = {en} }