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

• Published in: Applied thermal engineering Vol. 220; no. 119656; p. 119656
• Main Authors: Solai, Elie, Beaugendre, Héloïse, Bieder, Ulrich, Congedo, Pietro Marco
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 05.02.2023; Elsevier
• Subjects: Bayesian calibration; Fluid Dynamics; Immersion cooling; Kriging; Lithium-ion batteries; Numerical simulation; Physics; Surrogate model; Uncertainty quantification; Lithium-ion batteries; Bayesian calibration; Surrogate model; Immersion cooling; Uncertainty quantification; Numerical simulation; Kriging
• ISSN: 1359-4311; 1873-5606
• DOI: 10.1016/j.applthermaleng.2022.119656

This paper proposes an innovative way to deal with the uncertainties related to internal resistance of Li-ion batteries using experimental data and numerical simulation. First, a CFD model is used to reproduce 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. The impact of those uncertainties on the temperature evolution of Li-ion cells is quantified. A 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. The resulting temperature evolution computed with the two different resistance models is compared for the low state of charge situations. •A CFD model is validated against an experimental case of immersed Li-ion batteries.•Uncertainties on the internal resistance model parameters are modeled.•The solver’s temperature prediction is improved using Bayesian calibration.•The uncertainties in the temperature prediction are significantly reduced.•Overheating due to low state of charge is shown with an enhanced resistance model.