Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155511
Title: Thermal performance prediction of the battery surface via dynamic mode decomposition
Authors: Kanbur, Baris Burak
Kumtepeli, Volkan
Duan, Fei
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
IGS Journal Articles
MAE Journal Articles

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