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Reciprocating air flow for Li-ion battery thermal management to improve temperature uniformity

Mahamud, Rajib, Park, Chanwoo·2011·Journal of Power Sources

Abstract

Abstract The thermal management of traction battery systems for electrical-drive vehicles directly affects vehicle dynamic performance, long-term durability and cost of the battery systems. In this paper, a new battery thermal management method using a reciprocating air flow for cylindrical Li-ion (LiMn 2 O 4 /C) cells was numerically analyzed using (i) a two-dimensional computational fluid dynamics (CFD) model and (ii) a lumped-capacitance thermal model for battery cells and a flow network model. The battery heat generation was approximated by uniform volumetric joule and reversible (entropic) losses. The results of the CFD model were validated with the experimental results of in-line tube-bank systems which approximates the battery cell arrangement considered for this study. The numerical results showed that the reciprocating flow can reduce the cell temperature difference of the battery system by about 4 °C (72% reduction) and the maximum cell temperature by 1.5 °C for a reciprocation period of τ = 120 s as compared with the uni-directional flow case ( τ = ∞). Such temperature improvement attributes to the heat redistribution and disturbance of the boundary layers on the formed on the cells due to the periodic flow reversal.

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Metadata

Authors
Mahamud, R., & Park, C.
Venue
Journal of Power Sources
Publisher
Elsevier BV
Published
2011-07
Volume
196
Issue
13
Pages
5685-5696
ISSN
0378-7753 (ISSN-L)
OpenAlex
W1964935880
Cited by
787
References
0
FWCI
34.76
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Reciprocating air flow for Li-ion battery thermal management to improve temperature uniformity. https://app.cochat.ai/papers/10.1016/j.jpowsour.2011.02.076
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Mahamud, R., & Park, C. (2011). Reciprocating air flow for Li-ion battery thermal management to improve temperature uniformity. Journal of Power Sources. https://doi.org/10.1016/j.jpowsour.2011.02.076

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.