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Journal Articles Applied Thermal Engineering Year : 2023

Accuracy assessment of an internal resistance model of Li-ion batteries in immersion cooling configuration

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Abstract

Internal resistance is a critical parameter of the thermal behavior of Li-ion battery cells. This paper proposes an innovative way to deal with the uncertainties related to this physical parameter using experimental data and numerical simulation. First, a CFD model is validated against an experimental configuration representing the behavior of heated Li-ion battery cells under constant discharging current conditions. Secondly, an Uncertainty Quantification based methodology is proposed to represent the internal resistance and its inherent uncertainties. Thanks to an accurate and fast to compute surrogate model, the impact of those uncertainties on the temperature evolution of Li-ion cells is quantified. Finally, Bayesian inference of the internal resistance model parameters using experimental measurements is performed, reducing the prediction uncertainty by almost 95% for some temperatures of interest. Finally, an enhanced internal model is constructed by considering the state of charge and temperature dependency on internal resistance. This model is implemented in the CFD code and used to model a full discharge of the Li-ion batteries. The resulting temperature evolution computed with the two different resistance models is compared for the low state of charge situations.
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Dates and versions

hal-03878853 , version 1 (30-11-2022)

Identifiers

  • HAL Id : hal-03878853 , version 1

Cite

Elie Solai, Ulrich Bieder, Heloise Beaugendre, Pietro Marco Congedo. Accuracy assessment of an internal resistance model of Li-ion batteries in immersion cooling configuration. Applied Thermal Engineering, 2023, 220 (119656). ⟨hal-03878853⟩
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