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  4. Publikationen School of Engineering
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28346
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dc.contributor.authorBattaglia, Mattia-
dc.contributor.authorComi, Ennio-
dc.contributor.authorStadelmann, Thilo-
dc.contributor.authorHiestand, Roman-
dc.contributor.authorRuhstaller, Beat-
dc.contributor.authorKnapp, Evelyne-
dc.date.accessioned2023-07-27T09:08:57Z-
dc.date.available2023-07-27T09:08:57Z-
dc.date.issued2023-
dc.identifier.issn2770-9019de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28346-
dc.description.abstractPhysical models can help improve solar cell efficiency during the design phase as well as for quality control after the fabrication process. We present a data-driven approach to inverse modeling that can predict the underlying parameters of a finite element method (FEM) solar cell model based on an electroluminescence (EL) image of a solar cell with known cell geometry and laser scribed defects. For training the inverse model, 75,000 synthetic EL images were generated with randomized parameters of the physical cell model. We combine 17 deep convolutional neural networks (CNNs) based on a modified VGG19 architecture into a deep ensemble to add uncertainty estimates. Using the silicon solar cell model, we show that such a novel approach to data-driven statistical inverse modeling can help apply recent developments in deep learning to new engineering applications that require real-time parameterizations of physical models augmented by confidence intervals. The trained network was tested on four different physical solar cell samples and the estimated parameters were used to create the corresponding model representations. Resimulations of the measurements yielded relative deviations of the calculated and the measured junction voltage values of 0.2 \% on average with a maximum of 10 %, demonstrating the validity of the approach.de_CH
dc.language.isoende_CH
dc.publisherAIP Publishingde_CH
dc.relation.ispartofAPL Machine Learningde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectMachine learningde_CH
dc.subjectPVde_CH
dc.subjectCNNde_CH
dc.subjectInverse modellingde_CH
dc.subjectFinite element methodde_CH
dc.subjectParameter estimationde_CH
dc.subjectImagingde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titleDeep ensemble inverse model for image-based estimation of solar cell parametersde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
zhaw.organisationalunitInstitute of Computational Physics (ICP)de_CH
dc.identifier.doi10.1063/5.0139707de_CH
dc.identifier.doi10.21256/zhaw-28346-
zhaw.funding.euNode_CH
zhaw.issue3de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start036108de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume1de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.webfeedMultiphysics Modelingde_CH
zhaw.webfeedPhotonicsde_CH
zhaw.webfeedPhotovoltaikde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.webfeedZHAW sustainablede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

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Battaglia, M., Comi, E., Stadelmann, T., Hiestand, R., Ruhstaller, B., & Knapp, E. (2023). Deep ensemble inverse model for image-based estimation of solar cell parameters. APL Machine Learning, 1(3), 36108. https://doi.org/10.1063/5.0139707
Battaglia, M. et al. (2023) ‘Deep ensemble inverse model for image-based estimation of solar cell parameters’, APL Machine Learning, 1(3), p. 036108. Available at: https://doi.org/10.1063/5.0139707.
M. Battaglia, E. Comi, T. Stadelmann, R. Hiestand, B. Ruhstaller, and E. Knapp, “Deep ensemble inverse model for image-based estimation of solar cell parameters,” APL Machine Learning, vol. 1, no. 3, p. 036108, 2023, doi: 10.1063/5.0139707.
BATTAGLIA, Mattia, Ennio COMI, Thilo STADELMANN, Roman HIESTAND, Beat RUHSTALLER und Evelyne KNAPP, 2023. Deep ensemble inverse model for image-based estimation of solar cell parameters. APL Machine Learning. 2023. Bd. 1, Nr. 3, S. 036108. DOI 10.1063/5.0139707
Battaglia, Mattia, Ennio Comi, Thilo Stadelmann, Roman Hiestand, Beat Ruhstaller, and Evelyne Knapp. 2023. “Deep Ensemble Inverse Model for Image-Based Estimation of Solar Cell Parameters.” APL Machine Learning 1 (3): 36108. https://doi.org/10.1063/5.0139707.
Battaglia, Mattia, et al. “Deep Ensemble Inverse Model for Image-Based Estimation of Solar Cell Parameters.” APL Machine Learning, vol. 1, no. 3, 2023, p. 36108, https://doi.org/10.1063/5.0139707.


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