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dc.contributor.authorBo, Z
dc.contributor.authorLi, H
dc.contributor.authorYang, H
dc.contributor.authorLi, C
dc.contributor.authorWu, S
dc.contributor.authorXu, C
dc.contributor.authorXiong, G
dc.contributor.authorMariotti, D
dc.contributor.authorYan, J
dc.contributor.authorCen, K
dc.contributor.authorOstrikov, K
dc.date.accessioned2021-05-06T00:03:56Z
dc.date.available2021-05-06T00:03:56Z
dc.date.issued2021
dc.identifier.issn0017-9310
dc.identifier.doi10.1016/j.ijheatmasstransfer.2021.121075
dc.identifier.urihttp://hdl.handle.net/10072/404185
dc.description.abstractAccurately predicting thermal behavior is critically important in the real-world thermal management of supercapacitor modules with ultrahigh power and discharging current. In this work, an artificial intelligence approach based on the improved multiscale coupled electro-thermal model is employed for the first time to accurately predict the thermal behavior of a 350 F supercapacitor module under air-cooling conditions. Different from previous work that used commercial cells, the 350 F supercapacitors are fabricated from our proprietary pilot-scale production line. This approach provides a platform to precisely measure the structural parameters, electrical and thermal properties of electrodes and electrolytes (e.g., the temperature/current dependent equivalent series resistance and axial/radial thermal characteristics), which can improve the model for characterizing the irreversible heat generation and thermal transport processes. In particular, coupled with molecular dynamics simulations, the molecular origin of entropy is revealed via probing the atomic-level information (e.g., 1D/2D electric double-layer structure, electrical field/potential distributions, areal capacitance, and diffusion kinetics) to accurately predict the reversible heat generation. As a consequence, the deviation between our improved model and experimental results is substantially reduced to below 5%. A deep neural network based on the long short-term memory (LSTM) approach is trained to build a temperature database for practical supercapacitor modules under different operating conditions (including charging/discharging currents, cooling airflow rates, and cycle duration). This work demonstrates the potential of LSTM in predicting the thermal behavior, which can be broadly used for industry-relevant thermal management applications.
dc.description.peerreviewedYes
dc.languageen
dc.publisherElsevier BV
dc.relation.ispartofpagefrom121075
dc.relation.ispartofjournalInternational Journal of Heat and Mass Transfer
dc.relation.ispartofvolume171
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchPhysical Sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode02
dc.subject.fieldofresearchcode09
dc.titleCombinatorial atomistic-to-AI prediction and experimental validation of heating effects in 350 F supercapacitor modules
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationBo, Z; Li, H; Yang, H; Li, C; Wu, S; Xu, C; Xiong, G; Mariotti, D; Yan, J; Cen, K; Ostrikov, K, Combinatorial atomistic-to-AI prediction and experimental validation of heating effects in 350 F supercapacitor modules, International Journal of Heat and Mass Transfer, 2021, 171, pp. 121075
dc.date.updated2021-05-05T22:18:59Z
gro.hasfulltextNo Full Text
gro.griffith.authorOstrikov, Ken


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