001019896 001__ 1019896
001019896 005__ 20240709082038.0
001019896 037__ $$aFZJ-2023-05717
001019896 1001_ $$0P:(DE-Juel1)188978$$aMänken, Christian$$b0$$eCorresponding author$$ufzj
001019896 1112_ $$a18th International Symposium on Solid Oxide Fuel Cells$$cBoston$$d2023-05-28 - 2023-06-02$$gSOFC-XVIII$$wUSA
001019896 245__ $$aSolid Oxide Cell State-of-Health Prediction with Machine Learning
001019896 260__ $$c2023
001019896 3367_ $$033$$2EndNote$$aConference Paper
001019896 3367_ $$2BibTeX$$aINPROCEEDINGS
001019896 3367_ $$2DRIVER$$aconferenceObject
001019896 3367_ $$2ORCID$$aCONFERENCE_POSTER
001019896 3367_ $$2DataCite$$aOutput Types/Conference Poster
001019896 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1704184604_26653$$xAfter Call
001019896 520__ $$aTo better understand degradation in electrochemical converters and helping to correlate certain phenomena with specific operating conditions, machine learning (ML) methods are increasingly being applied. Success has already been achieved in the field of degradation analysis and prediction of capacity of Lithium ion batteries [1], for instance. In terms of Solix Oxide Cell (SOC) stacks ML methods have been applied mainly with the aim of identification of faulty operation modes and degradation related fault diagnosis [2]. ML approaches usually require a considerable amount of real training data, when used for forecasting models. A data consolidation and curation strategy was developed with the aim of processing the historic long-term test bench data of SOCs collected by Forschungszentrum Jülich over the past years. In comparison to other datasets developed in this field [3], the one presented in this work contains SOC stack tests in fuel cell operation with significantly longer operating times under load. A compilation of the sample experiments and the consolidation into a hierarchical data format are presented. Further, an essential part of the strategy is the automatic curation and analysis of electrochemical impedance spectroscopy (EIS) measurements, using a specifically developed procedure in Python. The varying quality of measurements from past years, as well as recurring artefacts such as parasitic inductances, can be addressed in this way. Additional distribution of relaxation times (DRT) deconvolutions and equivalent circuit modelling (ECM) are performed, as part of the procedure to automatically retrieve feature values from measurements. The novel dataset, which to the authors’ knowledge includes some of the longest SOC stack tests available, serves as the basis for several evaluations. In addition to classification and clustering work to derive degradation patterns, in particular based on the EIS data, another focus is on the development of forecasting models. The current work is primarily concerned with long short-term memory (LSTM), as well as regression models that make use of both the time series data and the characterisation measurements, such as EIS. References:[1]: Jones, P.K., Stimming, U. & Lee, A.A. Impedance-based forecasting of lithium-ion battery performance amid uneven usage. Nat Commun 13, 4806 (2022). [2]: B. Yang et al. Solid oxide fuel cell systems fault diagnosis: Critical summarization, classification, and perspectives. Journal of Energy Storage 34, 102153 (2021)[3]: A.K. Padinjarethil, S. Pollok & A. Hagen. Degradation studies using machine learning on novel solid oxide cell database. Fuel Cells. 21, 566–576 (2021)
001019896 536__ $$0G:(DE-HGF)POF4-1231$$a1231 - Electrochemistry for Hydrogen (POF4-123)$$cPOF4-123$$fPOF IV$$x0
001019896 536__ $$0G:(DE-Juel1)HITEC-20170406$$aHITEC - Helmholtz Interdisciplinary Doctoral Training in Energy and Climate Research (HITEC) (HITEC-20170406)$$cHITEC-20170406$$x1
001019896 65017 $$0V:(DE-MLZ)GC-110$$2V:(DE-HGF)$$aEnergy$$x0
001019896 7001_ $$0P:(DE-Juel1)201475$$aSchäfer, Christian$$b1
001019896 7001_ $$0P:(DE-Juel1)192282$$aKunz, Felix$$b2
001019896 7001_ $$0P:(DE-Juel1)156123$$aEichel, Rüdiger-A.$$b3$$eThesis advisor$$ufzj
001019896 909CO $$ooai:juser.fz-juelich.de:1019896$$pVDB
001019896 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188978$$aForschungszentrum Jülich$$b0$$kFZJ
001019896 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)188978$$aRWTH Aachen$$b0$$kRWTH
001019896 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)201475$$aForschungszentrum Jülich$$b1$$kFZJ
001019896 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)192282$$aForschungszentrum Jülich$$b2$$kFZJ
001019896 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)156123$$aForschungszentrum Jülich$$b3$$kFZJ
001019896 9101_ $$0I:(DE-588b)36225-6$$6P:(DE-Juel1)156123$$aRWTH Aachen$$b3$$kRWTH
001019896 9131_ $$0G:(DE-HGF)POF4-123$$1G:(DE-HGF)POF4-120$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1231$$aDE-HGF$$bForschungsbereich Energie$$lMaterialien und Technologien für die Energiewende (MTET)$$vChemische Energieträger$$x0
001019896 9141_ $$y2023
001019896 920__ $$lyes
001019896 9201_ $$0I:(DE-Juel1)IEK-9-20110218$$kIEK-9$$lGrundlagen der Elektrochemie$$x0
001019896 980__ $$aposter
001019896 980__ $$aVDB
001019896 980__ $$aI:(DE-Juel1)IEK-9-20110218
001019896 980__ $$aUNRESTRICTED
001019896 981__ $$aI:(DE-Juel1)IET-1-20110218