An Efficient Electrochemical-Thermal Tanks-in-Series Model for Lithium-Ion Batteries
Author(s) -
Akshay Subramaniam,
Suryanarayana Kolluri,
Shriram Santhanagopalan,
Venkat R. Subramanian
Publication year - 2020
Publication title -
journal of the electrochemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.258
H-Index - 271
eISSN - 1945-7111
pISSN - 0013-4651
DOI - 10.1149/1945-7111/aba700
Subject(s) - separator (oil production) , anode , computation , battery (electricity) , electrochemistry , cathode , energy storage , nuclear engineering , mechanics , heat transfer , voltage , series (stratigraphy) , energy balance , materials science , electrochemical cell , control theory (sociology) , thermodynamics , electrode , computer science , chemistry , electrical engineering , power (physics) , engineering , physics , algorithm , paleontology , biology , control (management) , artificial intelligence
This article extends the Tanks-in-Series methodology ( J. Electrochem. Soc. , 167 , 013534 (2020)) to generate an electrochemical-thermal model for Li-ion batteries. Energy balances based on porous electrode theory, including flux and generation terms, are volume-averaged for each region in a cathode-separator-anode representation, including current collectors. The original Tank model is thus augmented by a volume-averaged energy balance in each region, containing source terms and interfacial heat fluxes that are approximated accordingly. Cell-level quantities and temperature predictions are evaluated against a pseudo 2-dimensional (p2D) model. Acceptable voltage errors are achieved for relatively aggressive conditions of discharge and external heat transfer. Predictions of electrochemical variables are examined, and implications of the approximations discussed. The simplified methodology is also demonstrated for examples of large-scale models used in studying practical cell configurations. The Tanks-in-Series approach leads to a substantial reduction in equation system size, with attendant savings in computation time. This suggests potential in applications such as optimal charging, cell-balancing and estimation, and aids efforts to incorporate electrochemical models in advanced Battery Management Systems.
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