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Asymptotic reduction and homogenization of a thermo-electrochemical model for a lithium-ion battery

Abstract:
In this study, matched asymptotic expansions are used to systematically reduce a thermo-electrochemical model of a lithium-ion battery based on volume averaging the electrode microstructure. In the cases with a constant or oscillating applied current, explicit asymptotic solutions of the full model can be obtained. In the case with a constant cell potential, the reduced model comprises a low-order differential-algebraic system. The asymptotic and numerical solutions of the volume-averaged model are compared with the numerical solutions of a thermal pseudo-two-dimensional (P2D) model, which treats the electrode as a collection of spherical particles. Excellent agreement is found between the models at (dis)charge rates up to 2C, and reasonable agreement is found at 4C. Homogenization is then used to derive a thermal model of a battery comprising several connected lithium-ion cells. We derive a closed-form solution to the homogenized model when the effective Biot number is small, which corresponds to a spatially uniform battery temperature. By comparing simulation times, we show that the asymptotically reduced and homogenized models provide substantial computational savings compared with the full numerical simulations, thereby making them ideal for use in onboard thermal management systems. We also show that thermal runaway does not occur in the model, despite accounting for the Arrhenius dependence of the reaction coefficients.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.apm.2019.11.018

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Institution:
University of Oxford
Department:
Mathematical Institute
Department:
Unknown
Role:
Author
ORCID:
0000-0002-5928-6256


Publisher:
Elsevier
Journal:
Applied Mathematical Modelling More from this journal
Volume:
80
Pages:
724-754
Publication date:
2019-11-17
Acceptance date:
2019-11-13
DOI:
ISSN:
0307-904X


Language:
English
Keywords:
Pubs id:
pubs:1071552
UUID:
uuid:b7ed3427-9607-497e-bb1f-cb7b7edd4530
Local pid:
pubs:1071552
Source identifiers:
1071552
Deposit date:
2019-11-13
ARK identifier:

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