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|Title:||Thermal performance prediction of the battery surface via dynamic mode decomposition||Authors:||Kanbur, Baris Burak
|Keywords:||Engineering::Mechanical engineering||Issue Date:||2020||Source:||Kanbur, B. B., Kumtepeli, V. & Duan, F. (2020). Thermal performance prediction of the battery surface via dynamic mode decomposition. Energy, 201, 117642-. https://dx.doi.org/10.1016/j.energy.2020.117642||Journal:||Energy||Abstract:||The heat dissipation from the battery surface significantly affects battery performance and lifetime. This study proposes a new and an alternative method to predict the thermal performance of the battery operation according to the surface temperature gradients and heat & exergy losses by using a data-driven dynamic mode decomposition method, which is new for thermal flows. To predict the thermal gradients, a 10 min long experiment is performed via an infrared thermographic camera for a commercial Li-polymer battery of a smartphone. The camera collects the thermal images on the battery surface along 1 min as the data training period at first; then, the proposed method predicts the surface temperature gradients for the rest of the experimental period, 5 min. The temperature gradients on the battery surface are well predicted with less than 1% error whereas the heat dissipation and the exergy loss are predicted with the maximum error values of 2.75% and 5.30%, respectively. According to the error probability distribution plots, the vast majority of the occurred error is less than ±5%. The results prove the fast prediction ability of the proposed technique and show promising outcomes for further improvement studies.||URI:||https://hdl.handle.net/10356/155511||ISSN:||0360-5442||DOI:||10.1016/j.energy.2020.117642||Rights:||© 2020 Elsevier Ltd. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||ERI@N Journal Articles|
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